Human herpesvirus 6B (HHV-6B) detection and genome-wide host expression profiles implicate HHV-6B as a pulmonary pathogen after hematopoietic cell transplantation

Human herpesvirus 6B (HHV-6B) detection and genome-wide host expression profiles implicate HHV-6B as a pulmonary pathogen after hematopoietic cell transplantation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Human herpesvirus 6B (HHV-6B) detection and genome-wide host expression profiles implicate HHV-6B as a pulmonary pathogen after hematopoietic cell transplantation Joshua Hill, Yeon Joo Lee, Lisa Vande Vusse, Hu Xie, E Chung, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3361983/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Jan, 2024 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract Limited understanding of the immunopathogenesis of human herpesvirus 6B (HHV-6B) has prevented its acceptance as a pulmonary pathogen after hematopoietic cell transplantation (HCT). We conducted a prospective multicenter study of patients undergoing bronchoalveolar lavage (BAL) for pneumonia after allogeneic HCT. We tested blood and BAL fluid (BALF) for HHV-6B DNA and mRNA transcripts associated with lytic infection and performed RNA-seq on paired blood. Among 116 participants, HHV-6B DNA was detected in 37% of BALs, 49% of which had HHV-6B mRNA detection. We established an HHV-6B DNA threshold (≥2.3 log 10 copies/ml in BALF) that was highly predictive of HHV-6B mRNA detection and increased risk for death from respiratory failure (adjusted HR, 2.35; 95% CI, 1.08-5.11). Participants with HHV-6B DNA in BALF exhibited distinct host gene expression signatures, notable for enriched interferon signaling pathways in participants clinically diagnosed with idiopathic pneumonia. These data implicate HHV-6B as a pulmonary pathogen after allogeneic HCT. Health sciences/Medical research/Translational research Biological sciences/Microbiology/Virology/Herpes virus Biological sciences/Cancer/Haematological cancer Biological sciences/Microbiology/Infectious-disease diagnostics Health sciences/Diseases/Infectious diseases/Viral infection HHV-6 transplant pulmonary pneumonia RNA-sequencing Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Lower respiratory tract disease (LRTD) remains a leading cause of morbidity and mortality after allogeneic hematopoietic cell transplant (HCT). 1–4 Despite advances in pathogen-specific diagnostic methods, the causative pathogen is often elusive. Up to 10% of HCT recipients with LRTD are diagnosed with idiopathic pneumonia syndrome (IPS), 5 although current diagnostic approaches may miss potentially important pathogens. 6 Indeed, advances in diagnostic testing may have reduced the misclassification of infection-related LRTD as IPS, but IPS remains associated with high mortality. 7,8 Prior studies suggest that human herpesvirus 6B (HHV-6B) may be a pulmonary pathogen after allogeneic HCT. 9–16 HHV-6B is a DNA betaherpesvirus closely related to cytomegalovirus (CMV). HHV-6B infects most individuals during early childhood 17 and establishes latency in a broad range of cells, including bronchial glands. 18,19 HHV-6B DNA plasma detection (i.e., reactivation) occurs in approximately 40%-60% of HCT recipients within six weeks after HCT. 20,21 Small studies of heterogeneous populations using varied methods to detect HHV-6B in blood or bronchoalveolar lavage fluid (BALF) have inconsistently implicated HHV-6B as a potential pulmonary pathogen. 9–16,22 We previously performed a large single-center retrospective study and detected HHV-6B DNA in BALF from 27% of 553 allogeneic HCT recipients with LRTD. Patients with HHV-6B DNA in BALF had significantly increased risk of death compared to subjects without HHV-6B detection, with and without copathogens. 23 However, testing for HHV-6B in BALF is not routinely performed, and there are no guidelines for HHV-6B testing in the setting of LRTD despite available therapies. 2,24,25 The diagnosis of pathogens causing pneumonia often relies on detecting their genomic DNA or RNA using PCR, but this approach has poor specificity for pathogens prone to colonization and/or asymptomatic shedding. 26,27 Establishing a causal link between HHV-6B and LRTD is further hindered by diagnostic methods that do not distinguish between viral shedding and lytic (i.e., active) infection. For example, detection of messenger (m)RNA transcripts of CMV in BALF using reverse transcription quantitative PCR (RT-qPCR) had greater specificity for CMV pneumonia than genomic DNA detection by qPCR. 28 The additive value of testing for viral gene transcription, the correlation of HHV-6 detection in blood and BALF, and whether our previous findings of an association between HHV-6B detection in BALF and mortality remain pertinent in contemporary patients are unknown. Leveraging additional diagnostic approaches, such as genome-wide host expression profiling, may provide insight into the pathogen-specific immune pathways and further improve diagnostic strategies. Transcriptional profiling, an emerging tool for pathogen-associated disease assessment, can identify specific host signatures to discriminate between infectious and non-infectious causes of disease. 29–36 This approach has been used on blood and BALF to identify and discriminate between a broad range of pathogens in other contexts 37–44 and may be especially useful when the role of a pathogen is uncertain. 30 Few prospective studies have systematically preserved blood and BALF after allogeneic HCT for viral and host gene expression analyses. In this study, we prospectively enrolled adult allogeneic HCT recipients undergoing a BAL for evaluation of LRTD within 120 days of HCT from three centers and followed outcomes for up to 60 days after the BAL. We collected and tested paired blood and BALF samples with multiple molecular diagnostic techniques to comprehensively investigate the role of HHV-6B as a pulmonary pathogen after allogeneic HCT and determine the utility of whole blood gene expression signatures as a complement to pathogen-directed diagnostic testing. RESULTS Participant characteristics and LRTD diagnoses We prospectively enrolled 116 adult allogeneic HCT recipients undergoing 125 BAL events within 120 days after HCT at three institutions from July 2015 through December 2019 (Fig. 1 ). Plasma and BALF samples were not collected in one and three participants, respectively. Participant characteristics are described in Table 1 . The median number of days between hematopoietic cell infusion and BAL was 37 (interquartile range [IQR], 19–76). Table 1 Demographic and clinical variables at the time of the BAL Negative HHV-6B DNA in BAL (N = 71) Positive HHV6-B DNA in BAL (any level) (N = 42) a Positive HHV6-B DNA in BAL ≥ 2.3 log 10 copies/mL (N = 27) a Total (N = 113 b ) Age, years 60 27 (38%) 16 (38%) 10 (37%) 43 (38%) Female sex 27 (38%) 22 (52%) 13 (48%) 49 (43%) Race Caucasian 53 (75%) 33 (79%) 20 (74%) 86 (76%) Non-Caucasian 15 (21%) 9 (21%) 7 (26%) 24 (21%) Unknown 3 (4%) 0 (0%) 0 (0%) 3 (3%) Year of HCT 2015 6 (8%) 0 (0%) 0 (0%) 6 (5%) 2016 12 (17%) 5 (12%) 4 (15%) 17 (15%) 2017 22 (31%) 16 (38%) 11 (41%) 38 (34%) 2018 18 (25%) 7 (17%) 5 (19%) 25 (22%) 2019 13 (18%) 14 (33%) 7 (26%) 27 (24%) CMV serostatus D- and R- 10 (14%) 4 (10%) 3 (11%) 14 (12%) D + or R+ 61 (86%) 36 (86%) 22 (81%) 97 (86%) Missing 0 (0%) 2 (5%) 2 (7%) 2 (2%) HCT comorbidity index Score c 0 (low) 8 (11%) 3 (7%) 3 (11%) 11 (10%) 1–2 (intermediate) 20 (28%) 11 (26%) 8 (30%) 31 (27%) >=3 (high) 43 (61%) 28 (67%) 16 (59%) 71 (63%) HLA D/R status Matched related 9 (13%) 7 (17%) 6 (22%) 16 (14%) Matched unrelated 26 (37%) 14 (33%) 6 (22%) 40 (35%) Mismatched related 11 (15%) 8 (19%) 7 (26%) 19 (17%) Mismatched unrelated 24 (34%) 12 (29%) 7 (26%) 36 (32%) Missing 1 (1%) 1 (2%) 1 (4%) 2 (2%) Donor cell source Peripheral blood 51 (72%) 33 (79%) 18 (67%) 84 (74%) Bone marrow 16 (23%) 7 (17%) 7 (26%) 23 (20%) Umbilical cord blood 4 (6%) 2 (5%) 2 (7%) 6 (5%) Myeloablative conditioning d 32 (45%) 14 (33%) 10 (37%) 46 (41%) Maximum corticosteroid use pre-BAL e None 39 (55%) 17 (40%) 11 (41%) 56 (50%) 2 L/minute f 30 (42%) 21 (50%) 14 (52%) 51 (45%) WBC count pre-BAL g > 1,000 cells/mm 3 50 (70%) 31 (74%) 7 (26%) 81 (72%) ≤ 1,000 cells/mm 3 17 (24%) 10 (24%) 19 (70%) 27 (24%) Missing 4 (6%) 1 (2%) 1 (4%) 5 (4%) ALC pre-BAL g > 300 cells/mm 3 27 (38%) 11 (26%) 17 (63%) 38 (34%) ≤ 300 cells/mm 3 33 (46%) 26 (62%) 7 (26%) 59 (52%) Missing 11 (15%) 5 (12%) 3 (11%) 16 (14%) ANC pre-BAL g > 500 cells/mm 3 48 (68%) 29 (69%) 17 (63%) 77 (68%) ≤ 500 cells/mm 3 12 (17%) 8 (19%) 7 (26%) 20 (18%) Missing 11 (15%) 5 (12%) 3 (11%) 16 (14%) Day of BAL post-HCT (median, IQR ) 37 (15–68) 42 (20–79) 31 (20–72) 37 (19–76) LRTD cause h Bacterial 7 (10%) 3 (7%) 2 (7%) 10 (9%) Viral 12 (17%) 6 (14%) 3 (11%) 18 (16%) Fungal 16 (23%) 10 (24%) 8 (30%) 26 (23%) IPS 14 (20%) 4 (10%) 4 (15%) 18 (16%) Other 9 (13%) 2 (5%) 0 (0%) 11 (10%) Multifactorial 13 (18%) 17 (40%) 10 (37%) 30 (27%) Data are presented as number (percentage), unless otherwise indicated. HHV-6B indicates human herpesvirus 6B; BAL, bronchoalveolar lavage; D, donor; R, recipient; HLA, human leukocyte antigen; D, donor; R, recipient; WBC, white blood cell; ALC, absolute lymphocyte count; ANC, absolute neutrophil count; LRTD, lower respiratory tract disease; IPS, idiopathic pneumonia syndrome. a Groups are not mutually exclusive. b 3 of the 116 enrolled participants did not have a BAL fluid sample available for testing and are excluded from this Table and from the analyses that incorporate BAL fluid HHV-6B test results. c Based on the HCT-comorbidity index. d Myeloablative regimens included any regimen containing ≥ 800 cGY TBI, any regimen containing carmustine/etoposide/cytarabine/melphalan (BEAM), or any regimen containing busulfan/cyclophosphamide with or without antithymocyte globulin. e Within 14 days pre-BAL, based on prednisone-equivalent dosing. f Within 24 hours preceding the BAL. g Closest sample within 3 days pre-BAL. h Additional details on specific causes are in Table S1 . We defined LRTD as any abnormal radiographic finding with lower respiratory tract symptoms (see Online Methods). Bacterial, fungal, and viral causes were determined according to consensus guidelines. 2 , 45 – 47 Idiopathic pneumonia syndrome (IPS) was defined according to American Thoracic Society guidelines, requiring that no infection be suspected or detected. 48 If a microbiologic or non-infectious (i.e., IPS) cause of LRTD was not determined based on a standardized review of microbiologic, histopathologic, and other diagnostic criteria, the event was categorized as ‘other’. LRTD was most often due to multifactorial causes followed by invasive fungal pneumonia. Specific causes of LRTD are detailed in Table S1 . HHV-6B DNA detection was frequent in BALF and less common in paired plasma samples HHV-6B DNA was detected in 45 of 122 BALF samples (37%) (Fig. 1 ). In comparison, HHV-6B DNA was detected in 19 of 124 plasma samples (15%). Plasma detection of HHV-6B DNA had poor sensitivity (29.3%; 95% CI, 15.3%-43.2%) but high specificity (94.4%; 95% CI, 89%-99.7%) for BALF detection of HHV-6B DNA. Plasma and BALF viral load were moderately correlated ( Figure S1 ). The distribution of BALF HHV-6B DNA and mRNA detection over time and by LRTD category is depicted in Fig. 2 A-B. The proportion of BALs with HHV-6B DNA detection varied over time in a non-specific pattern. Participants with multiple causes of LRTD had the highest proportion of BALF HHV-6B detection (58%), and those with ‘other’ non-specific causes of LRTD had the lowest proportion of positivity (18%). The median BALF HHV-6B DNA viral load was 2.6 log 10 copies/mL (IQR, 2.0-3.4) and was highest in participants tested in the first 60 days after HCT (Fig. 2 C). Participants with ‘other’ non-specific causes of LRTD had the lowest viral loads (Fig. 2 D). We did not detect HHV-6 species A in any samples. Higher HHV-6B DNA viral load predicted HHV-6B mRNA detection in BALF Among the 45 BALF and 19 plasma samples with HHV-6B DNA detected, HHV-6B mRNA transcripts for either U38 or U90 were detected in 22 (49%) BALF and 16 (84%) whole blood samples; both transcripts were detected in 10 (22%) BALF and 12 (63%) whole blood samples ( Table S2 ). HHV-6B mRNA detection over time and by LRTD category had similar patterns as observed with HHV-6B DNA (Fig. 2 A-B; Figure S2). HHV-6B mRNA detection tended to occur in BALF obtained earlier after HCT (median, 21 days; IQR, 18–26 compared to HHV-6B negative BALF: median, 43 days; IQR, 20–81). We next aimed to examine the ability of HHV-6B viral load to distinguish between viral replication, a state during which mRNA is expressed, from viral shedding, during which mRNA expression is expected to be minimal or absent. We constructed ROC curves to measure the performance of BALF HHV-6B DNA viral loads for predicting HHV-6B mRNA expression. This demonstrated that HHV-6B DNA ≥ 2.3 log 10 copies/mL (218 copies/mL) in BALF had a high area under the curve (AUC) of 0.93 (95% CI, 0.89–0.98) for predicting detection of HHV-6B mRNA expression (U38 or U90; Figure S3A ) with a sensitivity, specificity, positive predictive value, and negative predictive value of 85%, 88%, 61%, and 96%, respectively ( Table S3 ). A BALF viral load threshold of ≥ 2.8 log 10 copies/mL (578 copies/mL) in BALF performed the best for predicting detection of both HHV-6B mRNA transcripts ( Figure S3B ). BALF HHV-6B DNA detection at levels that predict mRNA expression is associated with increased overall mortality and pulmonary death Among 113 participants with a tested BALF sample, 35 (31%) died for any reason and 27 (24%) died from respiratory failure within 60 days of the BAL. Participants with HHV-6B DNA detection at or above the threshold that predicts mRNA expression (≥ 2.3 log 10 copies/ml) in BALF (n = 27) had increased risk for overall mortality (adjusted hazard ratio [aHR], 1.81; 95% CI, 0.89–3.67; p = 0.099) and death from respiratory failure (aHR, 2.35; 95% CI, 1.08–5.11; p = 0.032) in models adjusted for age, maximum supplemental oxygen use (within 24 hours prior to the BAL), and maximum corticosteroid dose (within 14 days prior to the BAL) (Fig. 3 , Figure S4, Table S4 ). When stratified by a higher BALF HHV-6B DNA viral load threshold of ≥ 2.8 log 10 copies/mL that corresponded to detection of both mRNA transcripts, there was a stronger association with overall mortality (aHR, 2.86; 95% CI, 1.24–6.60; p = 0.014) and death from respiratory failure (aHR, 2.79; 95% CI, 1.11–7.03; p = 0.03). Genome-wide host expression profiling distinguishes between participants with and without HHV-6B detection in BALF We identified a subgroup of 54 participants who had a well-documented single cause of LRTD based on clinical testing (bacterial, n = 7; viral, n = 17; fungal, n = 17; IPS, n = 13) and a whole blood collection with sufficient quantity and quality of extracted RNA for bulk sequencing. One participant did not have a BALF sample available but is included in analyses. Characteristics of this cohort were similar to the overall cohort ( Table S5). Key biologic variables of interest are stratified by LRTD category and HHV-6B detection in Table S6 . HHV-6B ≥ 218 copies/mL in BALF was detected in 10 of 53 (19%) individuals in this subgroup; any level of plasma HHV-6B DNA was detected in 8 of 54 (15%) individuals. For gene expression comparisons, a BALF sample was considered positive for HHV-6B detection at levels ≥ 2.3 log 10 copies/mL. Only one participant in the bacterial subgroup had HHV-6B detection in BALF or blood, so this subgroup was not further analyzed. Because we measured transcriptional signals in the blood, we categorized individuals who had HHV-6B detection in plasma in the same group as those with BALF detection. A sensitivity analysis defining participants based solely on BALF detection of HHV-6B did not substantially alter our findings but slightly reduced power for detecting differentially expressed genes and pathways. We identified > 10,000 genes per comparison with sufficient expression count for analysis. Multidimensional scaling using principal components analysis (PCA) of the entire transcriptome suggested segregation between patients with viral LRTD who also had HHV-6B detection in BALF or plasma and those without HHV-6B detection; similar segregation by HHV-6B detection was evident in the IPS subgroup but not in the fungal subgroup (Fig. 4 A- 4 C). Patients with HHV-6B viremia appeared to more closely segregate with those who had HHV-6B detected in BALF, although comparisons were limited by the number of events. These findings indicate that in subjects with LRTD from another virus or LRTD without a clinically identified infectious cause (i.e., IPS), detection of HHV-6B DNA is associated with widespread differences in the host transcriptional response. We used DESeq2’s statistical approach to identify genes differentially expressed between subjects with and without HHV-6B detection within each LRTD group. Using a combined fold-change > +/-1.5 and an FDR < 5% threshold, we found 929 differentially regulated genes in subjects with versus without HHV-6B detection (434 up and 495 down) in the viral LRTD subgroup, 51 differentially expressed genes (46 up and 5 down) in the IPS subgroup, but none differentially expressed among patients with fungal LRTD. To further examine potential gene expression differences, we performed pathway analysis using Gene Set Enrichment Analysis (GSEA) to define distinct groups of genes that share common biological function, chromosomal location, or regulation in each LRTD category. In participants with a clinically determined viral cause of LRTD, additional detection of HHV-6B was associated with up-regulation of pathways involved in mitochondrial function/oxidative phosphorylation, oxidative stress, transcription and translation, cell cycle, viral infection, and several metabolic processes (Fig. 4 D). Intriguingly, down-regulated processes were highly enriched in immune responses, particularly T-cell receptor signaling and various interleukin-mediated pathways. In the IPS subgroup, participants with detection of HHV-6B had highly enriched interferon signaling pathways, as well as pathways associated with allograft rejection, oxidative phosphorylation, viral infection, cell cycle, transcription, and translation (Fig. 4 E). Down-regulated gene sets included Rho GTPase and Notch signaling, extracellular matrix/cellular remodeling and some immune-related pathways. In the fungal subgroup, participants with HHV-6B detection had widespread up-regulation of immune-related pathways, with the most significant mapping to interferon signaling as well as numerous other immuno-inflammatory programs (Fig. 4 F). DISCUSSION In this prospective, multicenter study of allogeneic HCT recipients with LRTD within 120 days of HCT, we demonstrate frequent detection of HHV-6B DNA and mRNA transcripts in BALF samples. HHV-6B was detected as a co-pathogen among individuals with other infectious causes of pneumonia and as the only pathogen in individuals thought to have an idiopathic non-infectious cause of pneumonia. Using HHV-6B DNA viral load thresholds identified to be predictive of HHV-6B mRNA transcription as a surrogate for active viral infection, we found that HHV-6B detection in BALF was independently associated with an increased risk for both overall mortality and death due to respiratory failure. Furthermore, genome-wide host gene expression profiling demonstrated distinct transcriptional signatures among participants with versus without BALF HHV-6B detection. Collectively, these results imply meaningful biological consequences when HHV-6B is detected in BALF from allogeneic HCT recipients with LRTD and support HHV-6B as a distinct pulmonary pathogen in this clinical context. HHV-6B is an important but incompletely understood pathogen in the setting of allogeneic HCT. It is the most frequent cause of infectious encephalitis, 49 but its role in other disease states is less well defined. Given its similarity to the closely related betaherpesvirus of CMV, a major cause of post-HCT pneumonitis prior to the implementation of diagnostic and preventive strategies, 50 there is a high likelihood that HHV-6B has similar pathogenicity. Indeed, in a unique study using a novel HCT mouse model infected with murine roseolovirus (a homolog of HHV-6), investigators demonstrated viral reactivation in the lung that induced an IPS-like phenotype. 51 These findings are concordant with a study demonstrating HHV-6 as the most frequently detected pathogen in individuals diagnosed with IPS after allogeneic HCT, who had worse outcomes than those without occult pathogen detection. 6 Identifying a potential role of HHV-6B in causing or contributing to LRTD is of significant clinical interest given the ready availability of diagnostic tests and antiviral therapies. In the current study, we found that 37% of BAL events had detectable HHV-6B DNA. The observation of generally higher viral loads and more frequent detection of HHV-6B in BALs obtained earlier after HCT are consistent with the epidemiology of HHV-6B reactivation and HHV-6B-encephalitis after HCT. 21 Detection of HHV-6B in BALF in the absence of concurrent detection in the plasma also points to independent, compartmentalized reactivation of HHV-6B within the lung parenchyma. A unique strength of this study was prospective collection and processing of BALF to allow testing for mRNA gene transcripts. We used targeted RT-qPCR to test for two HHV-6B-specific transcripts, U38 (a late DNA polymerase gene) and U90 (an immediate early gene), which have high sensitivity and specificity for viral replication in the context of lytic infection. 52 – 56 We found that HHV-6B DNA viral loads ≥ 2.3 and ≥ 2.8 log 10 copies/mL were highly predictive of detecting one or both mRNA transcripts, respectively. This evidence suggests that these thresholds may distinguish between viral shedding and lytic infection to reduce the ‘signal-to-noise’ ratio when testing cellular samples like BALF. Strikingly, these values are very similar to the HHV-6B DNA viral load threshold of 2.5 log 10 copies/mL identified in our aforementioned retrospective study, in which viral loads above this level were associated with mortality and death from respiratory failure with effect sizes similar to the current study. 23 Further evidence of tissue-invasive disease was demonstrated in our prior study using RNA in situ hybridization to detect HHV-6 gene expression in lung tissue from three patients with HHV-6B DNA in the BALF. Similar findings have been demonstrated for CMV in a study of allogeneic HCT recipients, wherein BALF CMV > 2.7 log 10 copies/mL (500 copies/mL) distinguished between CMV pneumonia and viral shedding without CMV pneumonia. 27 Additionally, that study demonstrated a distribution of CMV detection across LRTD categories similar to that observed for HHV-6B in the current analysis. 27 Host gene expression profiling is increasingly reported as a highly discriminatory technique for pathogen-associated disease assessment. 31 Pathogens trigger specific host transcriptional programs by interacting with pattern recognition receptors on leukocytes and other cells to mediate immune responses. Thus, unique transcriptional signatures may be defined to discriminate causes of LRTD, and this approach has been used with blood and BALF for a range of pathogens in other clinical contexts. 37 – 44 One study identified primary HHV-6 infection in healthy children as a driver of interferon and toll-like receptor signaling, 30 but studies in HCT recipients are lacking. In this study, we identified differential up- and down-regulation of whole blood gene expression pathways among patients with versus without HHV-6B detection in BALF. Notably, HHV-6B detection in participants with clinically diagnosed IPS and fungal LRTD was associated with a strong host interferon response and enrichment in a variety of pathways consistent with active viral infection, implicating HHV-6B as an independent but unrecognized cause of pneumonitis. Further support for the role of HHV-6B detection in the lungs as an active and distinct infection in participants clinically diagnosed with IPS was our observation through PCA that samples from patients with HHV-6B detection in BALF (but not plasma) clustered with samples from patients who had only plasma detection—and both groups segregated from HHV-6B negative samples. Strengths of this study include prospective enrollment and collection of paired BALF and plasma samples from a large cohort of contemporary HCT recipients at three centers. This is the first study to preserve BALF for RNA analysis to examine key insights into the correlation between HHV-6B DNA detection and viral transcription in HCT patients with LRTD. Because isolation and testing of mRNA is labor-intensive, not routinely available, and currently infeasible in a clinical setting, identification of a relevant genomic DNA threshold (for which testing is routinely available) is critical for clinical translation. Paired whole blood samples used for gene expression profiling generated additional observations in support of HHV-6B as a pulmonary pathogen and pave the way for similar approaches to disease definition in the HCT population. Although the transcriptional signal associated with HHV-6B detection was weaker in the fungal LRTD subgroup, GSEA’s statistical model allowed identification of significantly enriched pathways even when gene-specific differences were modest. This study also has limitations. There may be residual confounding in the analyses given the observational design, as well as the possibility of misclassification of LRTD categories based on clinical evaluation. Absence of HHV-6B mRNA detection in BALF could be due to variability in mRNA preservation, which could limit interpretation of the ROC analysis; nonetheless, the identified DNA viral load thresholds had clinical significance in the adjusted models. Gene expression analyses were limited by small sample sizes when stratified by HHV-6B detection and clinical disease subgroups precluding adjustment for other variables that could affect the results. While a randomized controlled prevention or treatment trial would provide the most definitive evidence for HHV-6B as a pulmonary pathogen, such trials would be very difficult, if not impossible, to conduct given the availability of effective antiviral agents and the sample sizes needed. Also, this level of evidence also does not exist for most microorganisms that are widely accepted as pulmonary pathogens after transplantation. Absent this, data from the current study and prior work support HHV-6B as a co-pathogen or primary pathogen in LRTD after allogeneic HCT. Considering commercial availability of similarly sensitive HHV-6 quantitative PCR assays and antiviral treatments (e.g., ganciclovir and foscarnet), there is opportunity to identify and treat affected patients. In conclusion, we used multiple molecular diagnostic techniques coupled with clinical observations to provide evidence that HHV-6B reactivation in the lungs after allogeneic HCT has immunologically distinct effects on host gene expression and measurable impact on mortality risk. Higher viral load correlated with detection of HHV-6B gene expression to suggest lytic infection and supported validation of viral load thresholds associated with clinical outcomes. Data from this study fulfill contemporary molecular guidelines for establishing disease causation, 57 and recognition of HHV-6B as a pulmonary pathogen has the potential for therapeutic targeting and improvement of outcomes after allogeneic HCT. ONLINE METHODS Participants We prospectively enrolled adult allogeneic HCT recipients undergoing a BAL for evaluation of LRTD within 120 days of HCT from Fred Hutchinson Cancer Center (Fred Hutch), City of Hope Comprehensive Cancer Center (COH), and Memorial Sloan Kettering Cancer Center (MSKCC). Data were abstracted from medical records for participant characteristics, microbiologic results, and outcomes for up to 60 days after the BAL date. The study was approved by the Fred Hutch Institutional Review Board and participants provided written informed consent. LRTD Categorization We defined LRTD as any abnormal radiographic findings with lower respiratory tract symptoms (e.g., cough, shortness of breath, or decreased oxygen saturation). Bacterial, fungal, and viral causes were defined according to consensus guidelines. 2 , 45 – 47 Idiopathic pneumonia syndrome (IPS), in which no microbiologically documented infection was detected or suspected, was defined according to the American Thoracic Society guideline. 48 Diffuse alveolar hemorrhage (DAH) was defined as increasingly bloody return from serial lavages or report of “bloody” BALF followed by treatment with corticosteroids. 2 If a microbiologic or non-infectious (i.e., idiopathic pneumonia syndrome [IPS]) cause of LRTD was not determined based on a standardized review of microbiologic, histopathologic, and other diagnostic criteria, we categorized the episode as ‘other’. All infectious and non-infectious causes of LRTD were based on review of clinical, microbiologic, histopathologic, and radiographic records. Final determinations were made by author J.A.H. Microbiologic testing The following tests were routinely performed on BALF throughout the study period: gram stain, fungal stain, and acid fast bacilli stain; cytology examination; conventional cultures for bacteria, fungi, mycobacteria, nocardia, and legionella; shell viral centrifugation viral cultures for CMV and/or PCR; and direct fluorescent antibody (DFA) testing and/or PCR for Pneumocystis jirovecii . Serum and BALF were routinely tested for Aspergillus with the galactomannan index (GMI) using the Bio-Rad Platelia Assay (Hercules, CA). 58 A GMI ≥ 0.5 with a confirmatory index processed separately on the same sample was considered positive for both BALF and serum. Additionally, multiplex quantitative reverse-transcription PCR panel was used to detect up to 12 respiratory viruses including influenza A, influenza B, RSV, PIV types 1 to 4, adenovirus, human metapneumovirus, coronavirus, rhinovirus, and bocavirus. 59 At MSKCC, the BioFire FilmArray Respiratory Panel (Salt Lake City, Utah) was used to detect up to 14 respiratory viruses in addition to Bordetella pertussis , Chlamydophila pneumoniae , and Mycoplasma pneumoniae. Procedures, Samples, and Testing At all sites, eligible patients routinely underwent a BAL, if clinically appropriate, using standard procedures based on international consensus guidelines. 60 Patients were not routinely tested or treated for HHV-6B in blood or BALF during the study period. Up to 15 mL of BALF were obtained in real-time during the bronchoscopy and immediately processed. 0.25 mL of fresh BALF were aliquoted and frozen at -80° Celsius. Using a minimum of 2 mL and up to 10 mL of BALF, a cell pellet was isolated, resuspended in RNAprotect Cell Reagent (QIAGEN, Germantown, MD), and stored at 4° Celsius for at least 12 hours and up to 72 hours) before storing at -80° Celsius. At the time of the BAL procedure, 6 mL of blood were collected in EDTA tubes for plasma isolation and 2.5 mL of blood in PAXgene tubes (QIAGEN, Germantown, MD). PAXgene tubes were stored according to the package insert. We extracted DNA from fresh BALF aliquots and plasma for HHV-6B testing using real-time quantitative polymerase chain reaction (qPCR) assays as previously described. 61 The assay has a lower limit of detection (LLD) of 10 copies/mL and a lower limit of quantitation (LLQ) of 50 copies/mL; a threshold of ≥ 50 copies/mL was considered positive. We performed qPCR for the human β-globin gene from BALF to confirm cellularity and the quality of DNA extraction. 9 , 27 We extracted RNA from BALF cell pellets using the RNAeasy Mini Kit (QIAGEN, Germantown, MD). We extracted RNA from whole blood stored in PAXgene tubes using the MagMAX™ for Stabilized Blood Tubes RNA Isolation Kit and GLOBINclear (Thermo Fisher Scientific, Bothell, WA) to deplete alpha and beta globin mRNA from whole blood samples according to the package inserts. DNase treatment was incorporated into both BAL and blood RNA extractions to reduce contamination by genomic DNA. We separately tested aliquots of 5 µL of eluted RNA from BAL and blood samples for two HHV-6B mRNA gene transcripts, U38 (late DNA polymerase gene) and U90 (immediate early gene), with a laboratory developed reverse transcription qPCR (RT-qPCR) assay. 55 Synthetically produced RNA transcripts were used for standards. Intra and inter-assay reproducibility were tested, and amplification of house-keeping genes were performed as controls. These targets were previously demonstrated to have high sensitivity and specificity for viral replication. 52 – 56 We also performed RT-qPCR for GAPDH to confirm the quality of RNA extraction. RNA concentration was assessed using Nanodrop (Thermo Fisher Scientific, Bothell, WA). Remaining RNA extracted from whole blood samples was processed in the Fred Hutch Genomics & Bioinformatics Shared Resource. We used the TruSeq RNA Exome, previously known as the TruSeq RNA Access kit (Illumina, San Diego, CA), to prepare cDNA libraries. RNA purity was assessed with an Agilent 4200 TapeStation (Agilent Technologies, Santa Clara, CA, USA). Additional library QC, blending of pooled indexed libraries, and cluster optimization was performed using Life Technologies’ Invitrogen Qubit® 2.0 Fluorometer (Life Technologies-Invitrogen, Carlsbad, CA, USA). Barcoded RNAseq libraries were pooled (54-plexed) and clustered onto an S1 flow cell. Sequencing was performed using an Illumina NovaSeq 6000 employing a paired-end, 50 base read length (PE50) sequencing strategy. Basecalling and demultiplexing of barcoded reads were performed with Illumina bcl2fastq v2.20. Read quality was summarized using FastQC 0.11.9. Paired reads were aligned to the UCSC GRCh38/hg38 human reference genome using STAR 2.7.7a. 62 Annotations from Gencode v38 were provided with the “--quantMode GeneCounts” option to generate per-gene counts during the alignment process. Only annotations for protein coding genes, polymorphic pseudogenes, and lncRNAs were considered. The second-strand read counts for each gene were combined into a single gene-count matrix for all samples. Statistical Analysis We described the distribution of demographic and clinical characteristics between patients with and without any HHV-6B detection in BALF, as well as the sensitivity and specificity of HHV-6B DNA detection in plasma for predicting HHV-6B DNA detection in BALF. We depicted the distribution of HHV-6B DNA and mRNA detection in BALF by time post-HCT and LRTD category. We generated receiver operating characteristic (ROC) curves to determine the ability of quantitative BALF HHV-6B DNA values to predict HHV-6B mRNA detection. We used the observed BALF HHV-6B DNA threshold from ROC curves that optimally predicted HHV-6B mRNA detection to define cases of possible HHV-6B pneumonitis. The results from HHV-6B DNA detection were used instead of mRNA detection to improve generalizability for potential clinical applications, as HHV-6 DNA testing is readily available whereas testing for mRNA is not. We generated Kaplan-Meier and cumulative incidence curves to estimate overall mortality and death due to respiratory causes (i.e., ‘pulmonary death’), respectively, by 30 and 60 days after BAL in patients with and without possible HHV-6B pneumonitis, irrespective of other causes of LRTD. In the cumulative incidence curves, non-pulmonary deaths were competing events. Univariate and multivariate Cox proportional hazards models were used to compare these outcomes by day 60 after the BAL event, adjusted for potential confounders, including other causes of LRTD. Two-sided P values < 0.05 were considered statistically significant. SAS version 9.3 (SAS institute, Cary, NC) was used for analyses. For RNA-Seq analyses, we identified subgroups of participants who had a well-documented clinically determined single cause of LRTD (i.e., bacterial, viral, fungal, or IPS). In each diagnostic category, we compared whole-transcriptome expression variability between patients with and without possible HHV-6B pneumonitis using Principal Component Analysis (PCA). We identified differentially expressed genes using the R package DESeq2. 63 Significant differential gene expression was based on a |log2 (ratio)| ≥ 0.585 (± 1.5-fold) difference and a false discovery rate (FDR) set to 5% to correct for multiple testing. 64 To identify the type of transcriptional programs activated in a given sample, we applied Gene Set Enrichment Analysis (GSEA) 65 using 50 Hallmark and 1329 Canonical Pathway gene sets curated from the Molecular Signature Database (MSigDB) 66 with a FDR threshold of 5% to denote significantly enriched pathways. Differentially up or down regulated gene sets were graphed based on their FDR and normalized enrichment scores using volcano plots. We performed a priori sample size calculations to estimate target sample sizes and power for detecting differences in overall mortality and gene expression based on preliminary data. We estimated that with a target sample size of approximately 150, we would identify up to 48 participants with any HHV-6B detection in BALF, about half of whom would have detectable HHV-6B mRNA transcripts (n = 24). To test the hypothesis that patients with possible HHV-6B LRTD will have higher mortality than patients without, we estimated that these sample sizes would be able to demonstrate a statistically significant difference if the hazard ratio was at least 2.5–2.6 for effect sizes ranging from 5–20%, with 80% power and a two-sided type I error rate of 0.05. For the subgroup analyses with RNA-Seq, we estimated identifying 10–15 participants per LRTD category who met the described criteria. A power analysis for 2-group comparisons using the RNA-seq Power R package 67 with parameters estimated from a representative RNA-seq dataset demonstrated that the study would be adequately powered (84%-95%) to detect meaningful changes in gene expression with ≥ 1.75-2 fold changes assuming a FDR of 5%. Declarations Data Availability The data that support the findings of this study are available from the corresponding author upon reasonable request. Prior presentation of the data This study was presented in part at IDWeek 2022 in Washington, DC. Financial support This work was supported by the National Institutes of Health [5K23AI119133 to J.A.H. and K24HL093294 to M.B.] and the American Society for Transplantation and Cellular Therapy/National Marrow Donor Program/Be The Match Foundation [Amy Strelzer Manasevit Research Program] to J.A.H. Additional resources were provided by the National Institutes of Health Fred Hutch/University of Washington/Seattle Children’s Cancer Consortium grant [P30 CA015704] and the Fred Hutch Genomics & Bioinformatics Shared Resource. Potential conflicts of interest JAH: Consulting for Allovir, Gilead, Karius, Symbio; research support from Allovir, Gilead, Karius, Merck. YJL: Research support from Karius, AiCuris, Scynexis, and Merck & Co Inc. AW: Consulting for Kyorin Pharmaceutical; research support from Allovir, Ansun Biopharma, Pfizer, Vir/GSK. GRH: Consulting for Generon Corporation, NapaJen Pharma, iTeos Therapeutics, Neoleukin Therapeutics, Commonwealth Serum Laboratories, Cynata Therapeutics; research support from Compass Therapeutics, Syndax Pharmaceuticals, Applied Molecular Transport, Serplus Technology, Heat Biologics, Laevoroc Oncology, iTEOS therapetics and Genentech. DMZ: Consulting for Allovir. MB: Consulting for Allovir, Symbio, Evrys Bio; research support from Merck. All other authors report no disclosures. Acknowledgements We acknowledge Jeffrey Delrow, Matt Fitzgibbon, Alyssa Dawson, and Pamela Yang for their help with bioinformatics and sample processing. Author contributions J.A.H., L.K.V-V., D.M.Z., A.W., W.M.L., G-S.C., G.H., and M.B. were responsible for the design of the study and/or interpretation of the data. J.A.H., H.X., W.M.L., and S.A.G. analyzed the data and created the figures. Data were collected by J.A.H., Y.J.L., L.K.V-V., E.L.C., and S.D. Laboratory work was carried out or supervised by M-L.H., H.Z., and K.R.J. J.A.H wrote the first draft. All authors contributed to the writing and revision of the manuscript and approved the final version. References Aguilar-Guisado, M. et al. Pneumonia in allogeneic stem cell transplantation recipients: a multicenter prospective study. Clin Transplant 25 , E629-38 (2011). Panoskaltsis-Mortari, A. et al. An official American Thoracic Society research statement: noninfectious lung injury after hematopoietic stem cell transplantation: idiopathic pneumonia syndrome. Am J Respir Crit Care Med 183 , 1262–79 (2011). Yen, K. T., Lee, A. S., Krowka, M. J. & Burger, C. D. Pulmonary complications in bone marrow transplantation: a practical approach to diagnosis and treatment. Clin Chest Med 25 , 189–201 (2004). Gooley, T. A. et al. Reduced mortality after allogeneic hematopoietic-cell transplantation. N Engl J Med 363 , 2091–101 (2010). Fukuda, T. et al. Risks and outcomes of idiopathic pneumonia syndrome after nonmyeloablative and conventional conditioning regimens for allogeneic hematopoietic stem cell transplantation. Blood 102 , 2777–85 (2003). Seo, S. et al. Idiopathic pneumonia syndrome after hematopoietic cell transplantation: Evidence of occult infectious etiologies. Blood (2015) doi:10.1182/blood-2014-12-617035. Wenger, D. S. et al. Incidence, Risk Factors, and Outcomes of Idiopathic Pneumonia Syndrome after Allogeneic Hematopoietic Cell Transplantation. Biol Blood Marrow Transplant 26 , 413–420 (2020). Englund, J. A. et al. Brief communication: fatal human metapneumovirus infection in stem-cell transplant recipients. Ann Intern Med 144 , 344–349 (2006). Cone, R. W. et al. Human herpesvirus 6 in lung tissue from patients with pneumonitis after bone marrow transplantation. N Engl J Med 329 , 156–61 (1993). Buchbinder, S., Elmaagacli, A. H., Schaefer, U. W. & Roggendorf, M. Human herpesvirus 6 is an important pathogen in infectious lung disease after allogeneic bone marrow transplantation. Bone Marrow Transplant 26 , 639–44 (2000). Nagate et al. Detection and quantification of human herpesvirus 6 genomes using bronchoalveolar lavage fluid in immunocompromised patients with interstitial pneumonia. Int J Mol Med 8 , 379 (2001). Nishimaki, K. et al. The possible involvement of human herpesvirus type 6 in obliterative bronchiolitis after bone marrow transplantation. Bone Marrow Transplant 32 , 1103–5 (2003). Costa, C. et al. Quantitative detection of HHV-6 and HHV-7 in transbronchial biopsies from lung transplant recipients. New Microbiol 34 , 275–80 (2011). Lautenschlager, I. & Razonable, R. R. Human herpesvirus-6 infections in kidney, liver, lung, and heart transplantation: review. Transpl Int 25 , 493–502 (2012). Carrigan, D. R. et al. Interstitial pneumonitis associated with human herpesvirus-6 infection after marrow transplantation. Lancet 338 , 147–9 (1991). Mariotte, E. et al. Significance of herpesvirus 6 in BAL fluid of hematology patients with acute respiratory failure. Infection 39 , 225–30 (2011). Zerr, D. M. et al. A population-based study of primary human herpesvirus 6 infection. N Engl J Med 352 , 768–76 (2005). Krueger, G. R. et al. Latent herpesvirus-6 in salivary and bronchial glands. Lancet 336 , 1255–6 (1990). De Bolle, L., Naesens, L. & De Clercq, E. Update on human herpesvirus 6 biology, clinical features, and therapy. Clin Microbiol Rev 18 , 217–45 (2005). Noviello, M. et al. Human herpesvirus 6-specific T-cell immunity in allogeneic hematopoietic stem cell transplant recipients. Blood Adv (2023) doi:10.1182/BLOODADVANCES.2022009274. Hill, J. A. et al. Kinetics of Double Stranded DNA Viremia after Allogeneic Hematopoietic Cell Transplantation. Clin Infect Dis (2017) doi:10.1093/cid/cix804. Seo, S. et al. Idiopathic pneumonia syndrome after hematopoietic cell transplantation: evidence of occult infectious etiologies. Blood 125 , 3789–97 (2015). Hill, J. A. et al. Human herpesvirus 6B and lower respiratory tract disease after hematopoietic cell transplantation. Journal of Clinical Oncology 37 , (2019). Tomblyn, M. et al. Guidelines for preventing infectious complications among hematopoietic cell transplantation recipients: a global perspective. Biol Blood Marrow Transplant 15 , 1143–238 (2009). Chemaly, R. F., Hill, J. A., Voigt, S. & Peggs, K. S. In vitro comparison of currently available and investigational antiviral agents against pathogenic human double-stranded DNA viruses: A systematic literature review. Antiviral Res 163 , (2019). Chellapandian, D. et al. Bronchoalveolar lavage and lung biopsy in patients with cancer and hematopoietic stem-cell transplantation recipients: a systematic review and meta-analysis. J Clin Oncol 33 , 501–9 (2015). Boeckh, M. et al. Cytomegalovirus (CMV) DNA Quantitation in Bronchoalveolar Lavage Fluid from Hematopoietic Stem Cell Transplant Recipients with CMV Pneumonia. Journal of Infectious Diseases 215 , 1514–1522 (2017). Boivin, G. et al. Quantitation of cytomegalovirus DNA and characterization of viral gene expression in bronchoalveolar cells of infected patients with and without pneumonitis. J Infect Dis 173 , 1304–12 (1996). Torres, J. et al. Application of Host Genome-wide Expression Profiles for Early Identification of Sepsis in Children with Cancer, Fever and Neutropenia at High Risk of Invasive Bacterial Infections. IDWeek Abstract 1459 (2012). Hu, X., Yu, J., Crosby, S. D. & Storch, G. A. Gene expression profiles in febrile children with defined viral and bacterial infection. Proc Natl Acad Sci U S A 110 , 12792–7 (2013). Ramilo, O. & Mejías, A. Shifting the paradigm: host gene signatures for diagnosis of infectious diseases. Cell Host Microbe 6 , 199–200 (2009). Zinter, M. S. et al. The pulmonary metatranscriptome prior to pediatric HCT identifies post-HCT lung injury. Blood 137 , 1679–1689 (2021). Zinter, M. S. et al. Pulmonary microbiome and gene expression signatures differentiate lung function in pediatric hematopoietic cell transplant candidates. Sci Transl Med 14 , (2022). Ramilo, O. et al. Gene expression patterns in blood leukocytes discriminate patients with acute infections. Blood 109 , 2066–2077 (2007). McClain, M. T. et al. A blood-based host gene expression assay for early detection of respiratory viral infection: an index-cluster prospective cohort study. Lancet Infect Dis 21 , 396–404 (2021). Suarez, N. M. et al. Superiority of transcriptional profiling over procalcitonin for distinguishing bacterial from viral lower respiratory tract infections in hospitalized adults. J Infect Dis 212 , 213–222 (2015). Meyer, K. C. Bronchoalveolar lavage as a diagnostic tool. Semin Respir Crit Care Med 28 , 546–60 (2007). Raju, B. et al. Gene expression profiles of bronchoalveolar cells in pulmonary TB. Tuberculosis (Edinb) 88 , 39–51 (2008). Colvin, J. M. et al. Detection of viruses in young children with fever without an apparent source. Pediatrics 130 , e1455-62 (2012). Zaas, A. K. et al. Gene expression signatures diagnose influenza and other symptomatic respiratory viral infections in humans. Cell Host Microbe 6 , 207–17 (2009). Popper, S. J. et al. Gene transcript abundance profiles distinguish Kawasaki disease from adenovirus infection. J Infect Dis 200 , 657–66 (2009). Ramilo, O. et al. Gene expression patterns in blood leukocytes discriminate patients with acute infections. Blood 109 , 2066–77 (2007). Stojanov, S. et al. Periodic fever, aphthous stomatitis, pharyngitis, and adenitis (PFAPA) is a disorder of innate immunity and Th1 activation responsive to IL-1 blockade. Proc Natl Acad Sci U S A 108 , 7148–53 (2011). Thompson, M. R., Kaminski, J. J., Kurt-Jones, E. A. & Fitzgerald, K. A. Pattern recognition receptors and the innate immune response to viral infection. Viruses 3 , 920–40 (2011). Baron, E. J. et al. A guide to utilization of the microbiology laboratory for diagnosis of infectious diseases: 2013 recommendations by the Infectious Diseases Society of America (IDSA) and the American Society for Microbiology (ASM)(a). Clin Infect Dis 57 , e22–e121 (2013). De Pauw, B. et al. Revised definitions of invasive fungal disease from the European Organization for Research and Treatment of Cancer/Invasive Fungal Infections Cooperative Group and the National Institute of Allergy and Infectious Diseases Mycoses Study Group (EORTC/MSG) C. Clin Infect Dis 46 , 1813–21 (2008). Ascioglu, S. et al. Defining opportunistic invasive fungal infections in immunocompromised patients with cancer and hematopoietic stem cell transplants: an international consensus. Clin Infect Dis 34 , 7–14 (2002). Panoskaltsis-Mortari, A. et al. An official American Thoracic Society research statement: noninfectious lung injury after hematopoietic stem cell transplantation: idiopathic pneumonia syndrome. Am J Respir Crit Care Med 183 , 1262–79 (2011). Toomey, D., Phan, T. L., Phan, T., Hill, J. A. & Zerr, D. M. Viral Encephalitis after Hematopoietic Cell Transplantation: A Systematic Review. Transplant Cell Ther (2023) doi:10.1016/J.JTCT.2023.06.022. Erard, V. et al. Reduced Mortality of Cytomegalovirus Pneumonia After Hematopoietic Cell Transplantation Due to Antiviral Therapy and Changes in Transplantation Practices. Clinical Infectious Diseases 61 , 31–39 (2015). Zhou, X. et al. First Onset Herpesviral Infection and Lung Injury in Allogeneic Hematopoietic Cell Transplantation. Am J Respir Crit Care Med rccm.201809-1635OC (2019) doi:10.1164/rccm.201809-1635OC. Bressollette-Bodin, C. et al. Quantification of two viral transcripts by real time PCR to investigate human herpesvirus type 6 active infection. J Clin Virol 59 , 94–9 (2014). Ihira, M. et al. Development of quantitative RT-PCR assays for detection of three classes of HHV-6B gene transcripts. J Med Virol 84 , 1388–95 (2012). Van den Bosch, G. et al. Development of reverse transcriptase PCR assays for detection of active human herpesvirus 6 infection. J Clin Microbiol 39 , 2308–10 (2001). Hill, J. A. et al. RNA sequencing of the in vivo human herpesvirus 6b transcriptome to identify targets for clinical assays distinguishing between latent and active infections. J Virol 93 , (2019). Peddu, V. et al. Inherited Chromosomally Integrated Human Herpesvirus 6 Demonstrates Tissue-Specific RNA Expression In Vivo That Correlates with an Increased Antibody Immune Response . J Virol 94 , (2019). Fredericks, D. N. & Relman, D. A. Sequence-based identification of microbial pathogens: a reconsideration of Koch’s postulates. Clin Microbiol Rev 9 , 18–33 (1996). Fisher, C. E. et al. The Serum Galactomannan Index Predicts Mortality in Hematopoietic Stem Cell Transplant Recipients With Invasive Aspergillosis. Clinical Infectious Diseases 57 , 1001–1004 (2013). Waghmare, A. et al. Supplemental Oxygen–Free Days in Hematopoietic Cell Transplant Recipients With Respiratory Syncytial Virus. J Infect Dis 216 , 1227–1234 (2017). Harris, B., Lowy, F. D., Stover, D. E. & Arcasoy, S. M. Diagnostic Bronchoscopy in Solid-Organ and Hematopoietic Stem Cell Transplantation. Ann Am Thorac Soc 10 , 39–49 (2013). Zerr, D. M. et al. Human Herpesvirus 6 Reactivation and Encephalitis in Allogeneic Bone Marrow Transplant Recipients. Clinical Infectious Diseases 33 , 763–771 (2001). Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29 , 15–21 (2013). Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15 , 550 (2014). Reiner, A., Yekutieli, D. & Benjamini, Y. Identifying differentially expressed genes using false discovery rate controlling procedures. Bioinformatics 19 , 368–75 (2003). Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26 , 139–40 (2010). Subramanian, A. et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences 102 , 15545–15550 (2005). Hart, S. N., Therneau, T. M., Zhang, Y., Poland, G. A. & Kocher, J.-P. Calculating sample size estimates for RNA sequencing data. J Comput Biol 20 , 970–8 (2013). Additional Declarations Yes there is potential Competing Interest. JAH: Consulting for Allovir, Gilead, Karius, Symbio; research support from Allovir, Gilead, Karius, Merck. YJL: Research support from Karius, AiCuris, Scynexis, and Merck & Co Inc. AW: Consulting for Kyorin Pharmaceutical; research support from Allovir, Ansun Biopharma, Pfizer, Vir/GSK. GRH: Consulting for Generon Corporation, NapaJen Pharma, iTeos Therapeutics, Neoleukin Therapeutics, Commonwealth Serum Laboratories, Cynata Therapeutics; research support from Compass Therapeutics, Syndax Pharmaceuticals, Applied Molecular Transport, Serplus Technology, Heat Biologics, Laevoroc Oncology, iTEOS therapetics and Genentech. DMZ: Consulting for Allovir. MB: Consulting for Allovir, Symbio, Evrys Bio; research support from Merck. All other authors report no disclosures. Supplementary Files SupplementSubmit.docx Supplement Cite Share Download PDF Status: Published Journal Publication published 16 Jan, 2024 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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21:05:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3361983/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3361983/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41467-024-44828-9","type":"published","date":"2024-01-16T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":43842134,"identity":"e33b5e23-13e5-45be-9b90-15c11f03a797","added_by":"auto","created_at":"2023-09-28 15:38:23","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":334788,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConsort diagram.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3361983/v1/31739353b6a9d1ddd52eeb1e.jpg"},{"id":43842130,"identity":"4bf70523-3ade-4bac-abed-e1a841433f36","added_by":"auto","created_at":"2023-09-28 15:38:23","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":491881,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe distribution of BALF HHV-6B DNA and mRNA detection over time and by LRTD category.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePanels A and B \u003c/strong\u003eshow data from 122 BAL events in 113 participants. The first bar in each time period or category indicates the proportion of BALs with HHV-6B DNA detection; the second bar indicates the proportion with HHV-6B mRNA detection, among those with HHV-6B DNA detection (samples without HHV-6B DNA detection were not tested for mRNA). \u003cstrong\u003ePanel C and D\u003c/strong\u003e show data from 45 BAL events (in 42 participants) that had HHV-6B DNA detection. LRTD indicates lower respiratory tract disease.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3361983/v1/503eaf6596c2341d8837ae55.jpg"},{"id":43842131,"identity":"255d354d-10e5-41e7-9d48-9ada9274e81d","added_by":"auto","created_at":"2023-09-28 15:38:23","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":463201,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan Meier and cumulative incidence plots of time to overall mortality and death due to respiratory failure, respectively, among patients with and without HHV-6B detection in the BAL.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants with HHV-6B\u003csup\u003e \u003c/sup\u003eDNA ≥218 copies/ml (2.3 log\u003csub\u003e10\u003c/sub\u003e copies/mL) in BALF based on the threshold predictive of mRNA detection (n=27) had increased overall mortality (adjusted hazard ratio [aHR], 1.81; 95% CI, 0.89-3.67; p=0.099) (\u003cstrong\u003eA\u003c/strong\u003e) and death from respiratory failure (aHR, 2.35; 95% CI, 1.08-5.11; p=0.032) (\u003cstrong\u003eB\u003c/strong\u003e) compared to those without HHV-6B\u003csup\u003e \u003c/sup\u003edetection in models adjusted for age, oxygen use, and steroid use at the time of BAL in a multivariable Cox regression model. Deaths due to other causes were considered competing events in the cumulative incidence curve. The first and second log-rank or Gray’s test statistics in the figures depict the unadjusted comparison of the curves at day 30 and day 60 post-BAL, respectively.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3361983/v1/0030358ccc7685d3c7635635.jpg"},{"id":43842133,"identity":"5f8902b0-4cc4-4aad-b00a-faef913d592b","added_by":"auto","created_at":"2023-09-28 15:38:23","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1017121,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGenome-wide host expression profiling distinguishes between patients with and without HHV-6B detection.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePanels A through C \u003c/strong\u003edepict multidimensional scaling using principal components analysis (PCA) of the entire transcriptome from whole blood to compare between participants with and without HHV-6B detection, among those with a specific clinically determined LRTD diagnosis. Among 17 participants with a viral pneumonia, PCA analysis suggested segregation between those with HHV-6B detection in the BAL and/or plasma (white highlight) versus those without \u003cstrong\u003e(A)\u003c/strong\u003e. Among 13 participants with IPS, PCA analysis also suggested segregation between those with HHV-6B detection in the BAL and/or plasma (white highlight) versus those without \u003cstrong\u003e(B)\u003c/strong\u003e. No clear pattern was seen among 17 participants with fungal pneumonia \u003cstrong\u003e(C)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePanels D through F \u003c/strong\u003edisplay volcano plots of results from pathway analysis using Gene Set Enrichment Analysis (GSEA) to define and compare transcriptional programs by HHV-6B detection status in the same subgroups as Panels A through C. Each plot depicts gene sets that are either significantly up-regulated (to the right of the plot) or down-regulated (to the left of the plot) in participants with HHV-6B detection in BALF and/or plasma compared to those without. A false discovery rate (FDR) \u0026lt;0.05 was used to designate significant enrichment of gene sets.\u003c/p\u003e\n\u003cp\u003eNote: Only one patient in the bacterial subgroup had HHV-6B detection in the BAL or plasma, so this group was not further analyzed.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3361983/v1/21baf5d7731274a16ec63a4d.jpg"},{"id":49992760,"identity":"6999538c-c17c-405c-8e4d-4bc5bb7de951","added_by":"auto","created_at":"2024-01-22 18:58:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1252747,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3361983/v1/6dc83165-5879-43c8-aca8-e80223df31da.pdf"},{"id":43842132,"identity":"bdd19b35-7d09-4f07-aa47-03a5f2ce9904","added_by":"auto","created_at":"2023-09-28 15:38:23","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":849139,"visible":true,"origin":"","legend":"\u003cp\u003eSupplement\u003c/p\u003e","description":"","filename":"SupplementSubmit.docx","url":"https://assets-eu.researchsquare.com/files/rs-3361983/v1/69a1ad989ca9a394ce4c46c1.docx"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e there is potential Competing Interest.\nJAH: Consulting for Allovir, Gilead, Karius, Symbio; research support from Allovir, Gilead, Karius, Merck. \r\nYJL: Research support from Karius, AiCuris, Scynexis, and Merck \u0026 Co Inc. \r\nAW: Consulting for Kyorin Pharmaceutical; research support from Allovir, Ansun Biopharma, Pfizer, Vir/GSK. \r\nGRH: Consulting for Generon Corporation, NapaJen Pharma, iTeos Therapeutics, Neoleukin Therapeutics, Commonwealth Serum Laboratories, Cynata Therapeutics; research support from Compass Therapeutics, Syndax Pharmaceuticals, Applied Molecular Transport, Serplus Technology, Heat Biologics, Laevoroc Oncology, iTEOS therapetics and Genentech.\r\nDMZ: Consulting for Allovir. \r\nMB: Consulting for Allovir, Symbio, Evrys Bio; research support from Merck.\r\nAll other authors report no disclosures.","formattedTitle":"Human herpesvirus 6B (HHV-6B) detection and genome-wide host expression profiles implicate HHV-6B as a pulmonary pathogen after hematopoietic cell transplantation","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eLower respiratory tract disease (LRTD) remains a leading cause of morbidity and mortality after allogeneic hematopoietic cell transplant (HCT).\u003csup\u003e1\u0026ndash;4\u003c/sup\u003e Despite advances in pathogen-specific diagnostic methods, the causative pathogen is often elusive. Up to 10% of HCT recipients with LRTD are diagnosed with idiopathic pneumonia syndrome (IPS),\u003csup\u003e5\u003c/sup\u003e although current diagnostic approaches may miss potentially important pathogens.\u003csup\u003e6\u003c/sup\u003e Indeed, advances in diagnostic testing may have reduced the misclassification of infection-related LRTD as IPS, but IPS remains associated with high mortality.\u003csup\u003e7,8\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003ePrior studies suggest that human herpesvirus 6B (HHV-6B) may be a pulmonary pathogen after allogeneic HCT.\u003csup\u003e9\u0026ndash;16\u003c/sup\u003e HHV-6B is a DNA betaherpesvirus closely related to cytomegalovirus (CMV). HHV-6B infects most individuals during early childhood\u003csup\u003e17\u003c/sup\u003e and establishes latency in a broad range of cells, including bronchial glands.\u003csup\u003e18,19\u003c/sup\u003e HHV-6B DNA plasma detection (i.e., reactivation) occurs in approximately 40%-60% of HCT recipients within six weeks after HCT.\u003csup\u003e20,21\u003c/sup\u003e Small studies of heterogeneous populations using varied methods to detect HHV-6B in blood or bronchoalveolar lavage fluid (BALF) have inconsistently implicated HHV-6B as a potential pulmonary pathogen.\u003csup\u003e9\u0026ndash;16,22\u003c/sup\u003e We previously performed a large single-center retrospective study and detected HHV-6B DNA in BALF from 27% of 553 allogeneic HCT recipients with LRTD. Patients with HHV-6B DNA in BALF had significantly increased risk of death compared to subjects without HHV-6B detection, with and without copathogens.\u003csup\u003e23\u003c/sup\u003e However, testing for HHV-6B in BALF is not routinely performed, and there are no guidelines for HHV-6B testing in the setting of LRTD despite available therapies.\u003csup\u003e2,24,25\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThe diagnosis of pathogens causing pneumonia often relies on detecting their genomic DNA or RNA using PCR, but this approach has poor specificity for pathogens prone to colonization and/or asymptomatic shedding.\u003csup\u003e26,27\u003c/sup\u003e Establishing a causal link between HHV-6B and LRTD is further hindered by diagnostic methods that do not distinguish between viral shedding and lytic (i.e., active) infection. For example, detection of messenger (m)RNA transcripts of CMV in BALF using reverse transcription quantitative PCR (RT-qPCR) had greater specificity for CMV pneumonia than genomic DNA detection by qPCR.\u003csup\u003e28\u003c/sup\u003e The additive value of testing for viral gene transcription, the correlation of HHV-6 detection in blood and BALF, and whether our previous findings of an association between HHV-6B detection in BALF and mortality remain pertinent in contemporary patients are unknown.\u003c/p\u003e\n\u003cp\u003eLeveraging additional diagnostic approaches, such as genome-wide host expression profiling, may provide insight into the pathogen-specific immune pathways and further improve diagnostic strategies. Transcriptional profiling, an emerging tool for pathogen-associated disease assessment, can identify specific host signatures to discriminate between infectious and non-infectious causes of disease.\u003csup\u003e29\u0026ndash;36\u003c/sup\u003e This approach has been used on blood and BALF to identify and discriminate between a broad range of pathogens in other contexts\u003csup\u003e37\u0026ndash;44\u003c/sup\u003e and may be especially useful when the role of a pathogen is uncertain.\u003csup\u003e30\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eFew prospective studies have systematically preserved blood and BALF after allogeneic HCT for viral and host gene expression analyses. In this study, we prospectively enrolled adult allogeneic HCT recipients undergoing a BAL for evaluation of LRTD within 120 days of HCT from three centers and followed outcomes for up to 60 days after the BAL. We collected and tested paired blood and BALF samples with multiple molecular diagnostic techniques to comprehensively investigate the role of HHV-6B as a pulmonary pathogen after allogeneic HCT and determine the utility of whole blood gene expression signatures as a complement to pathogen-directed diagnostic testing.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\n\u003ch2\u003eParticipant characteristics and LRTD diagnoses\u003c/h2\u003e\n\u003cp\u003eWe prospectively enrolled 116 adult allogeneic HCT recipients undergoing 125 BAL events within 120 days after HCT at three institutions from July 2015 through December 2019 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Plasma and BALF samples were not collected in one and three participants, respectively. Participant characteristics are described in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The median number of days between hematopoietic cell infusion and BAL was 37 (interquartile range [IQR], 19\u0026ndash;76).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDemographic and clinical variables at the time of the BAL\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNegative HHV-6B\u003c/p\u003e\n\u003cp\u003eDNA in BAL (N\u0026thinsp;=\u0026thinsp;71)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePositive HHV6-B DNA in BAL (any level) (N\u0026thinsp;=\u0026thinsp;42)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePositive HHV6-B DNA in BAL\u0026thinsp;\u0026ge;\u0026thinsp;2.3 log\u003csub\u003e10\u003c/sub\u003e copies/mL (N\u0026thinsp;=\u0026thinsp;27)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;113\u003csup\u003eb\u003c/sup\u003e)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAge, years\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;=20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21\u0026ndash;40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17 (24%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15 (36%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10 (37%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32 (28%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e41\u0026ndash;60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27 (38%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10 (24%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6 (22%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37 (33%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27 (38%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16 (38%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10 (37%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e43 (38%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eFemale sex\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27 (38%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22 (52%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13 (48%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49 (43%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eRace\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCaucasian\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53 (75%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33 (79%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20 (74%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e86 (76%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNon-Caucasian\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15 (21%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9 (21%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7 (26%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24 (21%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnknown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eYear of HCT\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6 (8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6 (5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2016\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12 (17%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (12%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (15%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17 (15%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2017\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22 (31%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16 (38%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11 (41%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38 (34%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18 (25%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7 (17%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (19%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25 (22%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13 (18%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14 (33%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7 (26%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27 (24%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCMV serostatus\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eD- and R-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10 (14%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (10%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (11%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14 (12%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eD\u0026thinsp;+\u0026thinsp;or R+\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e61 (86%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36 (86%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22 (81%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e97 (86%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMissing\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eHCT comorbidity index Score\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003ec\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (low)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8 (11%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (11%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11 (10%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u0026ndash;2 (intermediate)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20 (28%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11 (26%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8 (30%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31 (27%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026gt;=3 (high)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e43 (61%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28 (67%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16 (59%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e71 (63%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eHLA D/R status\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMatched related\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9 (13%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7 (17%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6 (22%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16 (14%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMatched unrelated\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26 (37%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14 (33%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6 (22%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e40 (35%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMismatched related\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11 (15%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8 (19%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7 (26%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19 (17%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMismatched unrelated\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24 (34%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12 (29%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7 (26%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36 (32%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMissing\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eDonor cell source\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePeripheral blood\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e51 (72%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33 (79%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18 (67%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e84 (74%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBone marrow\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16 (23%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7 (17%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7 (26%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23 (20%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUmbilical cord blood\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6 (5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMyeloablative conditioning\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003ed\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32 (45%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14 (33%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10 (37%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46 (41%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMaximum corticosteroid\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003euse pre-BAL\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003ee\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39 (55%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17 (40%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11 (41%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e56 (50%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;1 mg/kg/day\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13 (18%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18 (43%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12 (44%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31 (27%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ge;\u0026thinsp;1 mg/kg/day\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19 (27%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7 (17%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (15%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26 (23%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMaximum oxygen use pre-BAL\u0026thinsp;\u0026gt;\u0026thinsp;2 L/minute\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003ef\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30 (42%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21 (50%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14 (52%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e51 (45%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eWBC count pre-BAL\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003eg\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026gt;\u0026thinsp;1,000 cells/mm\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50 (70%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31 (74%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7 (26%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e81 (72%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026le;\u0026thinsp;1,000 cells/mm\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17 (24%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10 (24%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19 (70%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27 (24%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMissing\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eALC pre-BAL\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003eg\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026gt;\u0026thinsp;300 cells/mm\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27 (38%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11 (26%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17 (63%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38 (34%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026le;\u0026thinsp;300 cells/mm\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33 (46%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26 (62%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7 (26%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e59 (52%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMissing\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11 (15%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (12%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (11%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16 (14%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eANC pre-BAL\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003eg\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026gt;\u0026thinsp;500 cells/mm\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48 (68%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29 (69%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17 (63%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e77 (68%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026le;\u0026thinsp;500 cells/mm\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12 (17%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8 (19%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7 (26%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20 (18%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMissing\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11 (15%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (12%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (11%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16 (14%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eDay of BAL post-HCT (median, IQR\u003c/strong\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37 (15\u0026ndash;68)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e42 (20\u0026ndash;79)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31 (20\u0026ndash;72)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37 (19\u0026ndash;76)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eLRTD cause\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003eh\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBacterial\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7 (10%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10 (9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eViral\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12 (17%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6 (14%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (11%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18 (16%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFungal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16 (23%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10 (24%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8 (30%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26 (23%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIPS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14 (20%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (10%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (15%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18 (16%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9 (13%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11 (10%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMultifactorial\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13 (18%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17 (40%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10 (37%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30 (27%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\" align=\"left\"\u003e\n\u003cp\u003eData are presented as number (percentage), unless otherwise indicated.\u003c/p\u003e\n\u003cp\u003eHHV-6B indicates human herpesvirus 6B; BAL, bronchoalveolar lavage; D, donor; R, recipient; HLA, human leukocyte antigen; D, donor; R, recipient; WBC, white blood cell; ALC, absolute lymphocyte count; ANC, absolute neutrophil count; LRTD, lower respiratory tract disease; IPS, idiopathic pneumonia syndrome.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eGroups are not mutually exclusive.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u003c/sup\u003e3 of the 116 enrolled participants did not have a BAL fluid sample available for testing and are excluded from this Table and from the analyses that incorporate BAL fluid HHV-6B test results.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ec\u003c/sup\u003eBased on the HCT-comorbidity index.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ed\u003c/sup\u003eMyeloablative regimens included any regimen containing\u0026thinsp;\u0026ge;\u0026thinsp;800 cGY TBI, any regimen containing carmustine/etoposide/cytarabine/melphalan (BEAM), or any regimen containing busulfan/cyclophosphamide with or without antithymocyte globulin.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ee\u003c/sup\u003eWithin 14 days pre-BAL, based on prednisone-equivalent dosing.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ef\u003c/sup\u003eWithin 24 hours preceding the BAL.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eg\u003c/sup\u003eClosest sample within 3 days pre-BAL.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eh\u003c/sup\u003eAdditional details on specific causes are in Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eWe defined LRTD as any abnormal radiographic finding with lower respiratory tract symptoms (see Online Methods). Bacterial, fungal, and viral causes were determined according to consensus guidelines.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e Idiopathic pneumonia syndrome (IPS) was defined according to American Thoracic Society guidelines, requiring that no infection be suspected or detected.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e If a microbiologic or non-infectious (i.e., IPS) cause of LRTD was not determined based on a standardized review of microbiologic, histopathologic, and other diagnostic criteria, the event was categorized as \u0026lsquo;other\u0026rsquo;. LRTD was most often due to multifactorial causes followed by invasive fungal pneumonia. Specific causes of LRTD are detailed in \u003cstrong\u003eTable \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/strong\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003eHHV-6B DNA detection was frequent in BALF and less common in paired plasma samples\u003c/h2\u003e\n\u003cp\u003eHHV-6B DNA was detected in 45 of 122 BALF samples (37%) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). In comparison, HHV-6B DNA was detected in 19 of 124 plasma samples (15%). Plasma detection of HHV-6B DNA had poor sensitivity (29.3%; 95% CI, 15.3%-43.2%) but high specificity (94.4%; 95% CI, 89%-99.7%) for BALF detection of HHV-6B DNA. Plasma and BALF viral load were moderately correlated (\u003cstrong\u003eFigure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/strong\u003e The distribution of BALF HHV-6B DNA and mRNA detection over time and by LRTD category is depicted in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA-B. The proportion of BALs with HHV-6B DNA detection varied over time in a non-specific pattern. Participants with multiple causes of LRTD had the highest proportion of BALF HHV-6B detection (58%), and those with \u0026lsquo;other\u0026rsquo; non-specific causes of LRTD had the lowest proportion of positivity (18%). The median BALF HHV-6B DNA viral load was 2.6 log\u003csub\u003e10\u003c/sub\u003e copies/mL (IQR, 2.0-3.4) and was highest in participants tested in the first 60 days after HCT (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC). Participants with \u0026lsquo;other\u0026rsquo; non-specific causes of LRTD had the lowest viral loads (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD). We did not detect HHV-6 species A in any samples.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eHigher HHV-6B DNA viral load predicted HHV-6B mRNA detection in BALF\u003c/h3\u003e\n\u003cp\u003eAmong the 45 BALF and 19 plasma samples with HHV-6B DNA detected, HHV-6B mRNA transcripts for either U38 or U90 were detected in 22 (49%) BALF and 16 (84%) whole blood samples; both transcripts were detected in 10 (22%) BALF and 12 (63%) whole blood samples (\u003cstrong\u003eTable S2\u003c/strong\u003e). HHV-6B mRNA detection over time and by LRTD category had similar patterns as observed with HHV-6B DNA (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA-B; \u003cstrong\u003eFigure S2).\u003c/strong\u003e HHV-6B mRNA detection tended to occur in BALF obtained earlier after HCT (median, 21 days; IQR, 18\u0026ndash;26 compared to HHV-6B negative BALF: median, 43 days; IQR, 20\u0026ndash;81).\u003c/p\u003e\n\u003cp\u003eWe next aimed to examine the ability of HHV-6B viral load to distinguish between viral replication, a state during which mRNA is expressed, from viral shedding, during which mRNA expression is expected to be minimal or absent. We constructed ROC curves to measure the performance of BALF HHV-6B DNA viral loads for predicting HHV-6B mRNA expression. This demonstrated that HHV-6B DNA\u0026thinsp;\u0026ge;\u0026thinsp;2.3 log\u003csub\u003e10\u003c/sub\u003e copies/mL (218 copies/mL) in BALF had a high area under the curve (AUC) of 0.93 (95% CI, 0.89\u0026ndash;0.98) for predicting detection of HHV-6B mRNA expression (U38 or U90; \u003cstrong\u003eFigure S3A\u003c/strong\u003e) with a sensitivity, specificity, positive predictive value, and negative predictive value of 85%, 88%, 61%, and 96%, respectively (\u003cstrong\u003eTable S3\u003c/strong\u003e). A BALF viral load threshold of \u0026ge;\u0026thinsp;2.8 log\u003csub\u003e10\u003c/sub\u003e copies/mL (578 copies/mL) in BALF performed the best for predicting detection of both HHV-6B mRNA transcripts (\u003cstrong\u003eFigure S3B\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBALF HHV-6B DNA detection at levels that predict mRNA expression is associated with increased overall mortality and pulmonary death\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong 113 participants with a tested BALF sample, 35 (31%) died for any reason and 27 (24%) died from respiratory failure within 60 days of the BAL. Participants with HHV-6B DNA detection at or above the threshold that predicts mRNA expression (\u0026ge;\u0026thinsp;2.3 log\u003csub\u003e10\u003c/sub\u003e copies/ml) in BALF (n\u0026thinsp;=\u0026thinsp;27) had increased risk for overall mortality (adjusted hazard ratio [aHR], 1.81; 95% CI, 0.89\u0026ndash;3.67; p\u0026thinsp;=\u0026thinsp;0.099) and death from respiratory failure (aHR, 2.35; 95% CI, 1.08\u0026ndash;5.11; p\u0026thinsp;=\u0026thinsp;0.032) in models adjusted for age, maximum supplemental oxygen use (within 24 hours prior to the BAL), and maximum corticosteroid dose (within 14 days prior to the BAL) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cstrong\u003eFigure S4, Table S4\u003c/strong\u003e). When stratified by a higher BALF HHV-6B DNA viral load threshold of \u0026ge;\u0026thinsp;2.8 log\u003csub\u003e10\u003c/sub\u003e copies/mL that corresponded to detection of both mRNA transcripts, there was a stronger association with overall mortality (aHR, 2.86; 95% CI, 1.24\u0026ndash;6.60; p\u0026thinsp;=\u0026thinsp;0.014) and death from respiratory failure (aHR, 2.79; 95% CI, 1.11\u0026ndash;7.03; p\u0026thinsp;=\u0026thinsp;0.03).\u003c/p\u003e\n\u003ch3\u003eGenome-wide host expression profiling distinguishes between participants with and without HHV-6B detection in BALF\u003c/h3\u003e\n\u003cp\u003eWe identified a subgroup of 54 participants who had a well-documented single cause of LRTD based on clinical testing (bacterial, n\u0026thinsp;=\u0026thinsp;7; viral, n\u0026thinsp;=\u0026thinsp;17; fungal, n\u0026thinsp;=\u0026thinsp;17; IPS, n\u0026thinsp;=\u0026thinsp;13) and a whole blood collection with sufficient quantity and quality of extracted RNA for bulk sequencing. One participant did not have a BALF sample available but is included in analyses. Characteristics of this cohort were similar to the overall cohort (\u003cstrong\u003eTable S5).\u003c/strong\u003e Key biologic variables of interest are stratified by LRTD category and HHV-6B detection in \u003cstrong\u003eTable S6\u003c/strong\u003e. HHV-6B\u0026thinsp;\u0026ge;\u0026thinsp;218 copies/mL in BALF was detected in 10 of 53 (19%) individuals in this subgroup; any level of plasma HHV-6B DNA was detected in 8 of 54 (15%) individuals. For gene expression comparisons, a BALF sample was considered positive for HHV-6B detection at levels\u0026thinsp;\u0026ge;\u0026thinsp;2.3 log\u003csub\u003e10\u003c/sub\u003e copies/mL. Only one participant in the bacterial subgroup had HHV-6B detection in BALF or blood, so this subgroup was not further analyzed. Because we measured transcriptional signals in the blood, we categorized individuals who had HHV-6B detection in plasma in the same group as those with BALF detection. A sensitivity analysis defining participants based solely on BALF detection of HHV-6B did not substantially alter our findings but slightly reduced power for detecting differentially expressed genes and pathways.\u003c/p\u003e\n\u003cp\u003eWe identified\u0026thinsp;\u0026gt;\u0026thinsp;10,000 genes per comparison with sufficient expression count for analysis. Multidimensional scaling using principal components analysis (PCA) of the entire transcriptome suggested segregation between patients with viral LRTD who also had HHV-6B detection in BALF or plasma and those without HHV-6B detection; similar segregation by HHV-6B detection was evident in the IPS subgroup but not in the fungal subgroup (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA-\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC). Patients with HHV-6B viremia appeared to more closely segregate with those who had HHV-6B detected in BALF, although comparisons were limited by the number of events. These findings indicate that in subjects with LRTD from another virus or LRTD without a clinically identified infectious cause (i.e., IPS), detection of HHV-6B DNA is associated with widespread differences in the host transcriptional response.\u003c/p\u003e\n\u003cp\u003eWe used DESeq2\u0026rsquo;s statistical approach to identify genes differentially expressed between subjects with and without HHV-6B detection within each LRTD group. Using a combined fold-change \u0026gt; +/-1.5 and an FDR\u0026thinsp;\u0026lt;\u0026thinsp;5% threshold, we found 929 differentially regulated genes in subjects with versus without HHV-6B detection (434 up and 495 down) in the viral LRTD subgroup, 51 differentially expressed genes (46 up and 5 down) in the IPS subgroup, but none differentially expressed among patients with fungal LRTD.\u003c/p\u003e\n\u003cp\u003eTo further examine potential gene expression differences, we performed pathway analysis using Gene Set Enrichment Analysis (GSEA) to define distinct groups of genes that share common biological function, chromosomal location, or regulation in each LRTD category. In participants with a clinically determined viral cause of LRTD, additional detection of HHV-6B was associated with up-regulation of pathways involved in mitochondrial function/oxidative phosphorylation, oxidative stress, transcription and translation, cell cycle, viral infection, and several metabolic processes (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eD). Intriguingly, down-regulated processes were highly enriched in immune responses, particularly T-cell receptor signaling and various interleukin-mediated pathways. In the IPS subgroup, participants with detection of HHV-6B had highly enriched interferon signaling pathways, as well as pathways associated with allograft rejection, oxidative phosphorylation, viral infection, cell cycle, transcription, and translation (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eE). Down-regulated gene sets included Rho GTPase and Notch signaling, extracellular matrix/cellular remodeling and some immune-related pathways. In the fungal subgroup, participants with HHV-6B detection had widespread up-regulation of immune-related pathways, with the most significant mapping to interferon signaling as well as numerous other immuno-inflammatory programs (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eF).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this prospective, multicenter study of allogeneic HCT recipients with LRTD within 120 days of HCT, we demonstrate frequent detection of HHV-6B DNA and mRNA transcripts in BALF samples. HHV-6B was detected as a co-pathogen among individuals with other infectious causes of pneumonia and as the only pathogen in individuals thought to have an idiopathic non-infectious cause of pneumonia. Using HHV-6B DNA viral load thresholds identified to be predictive of HHV-6B mRNA transcription as a surrogate for active viral infection, we found that HHV-6B detection in BALF was independently associated with an increased risk for both overall mortality and death due to respiratory failure. Furthermore, genome-wide host gene expression profiling demonstrated distinct transcriptional signatures among participants with versus without BALF HHV-6B detection. Collectively, these results imply meaningful biological consequences when HHV-6B is detected in BALF from allogeneic HCT recipients with LRTD and support HHV-6B as a distinct pulmonary pathogen in this clinical context.\u003c/p\u003e \u003cp\u003eHHV-6B is an important but incompletely understood pathogen in the setting of allogeneic HCT. It is the most frequent cause of infectious encephalitis,\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e but its role in other disease states is less well defined. Given its similarity to the closely related betaherpesvirus of CMV, a major cause of post-HCT pneumonitis prior to the implementation of diagnostic and preventive strategies,\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e there is a high likelihood that HHV-6B has similar pathogenicity. Indeed, in a unique study using a novel HCT mouse model infected with murine roseolovirus (a homolog of HHV-6), investigators demonstrated viral reactivation in the lung that induced an IPS-like phenotype.\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e These findings are concordant with a study demonstrating HHV-6 as the most frequently detected pathogen in individuals diagnosed with IPS after allogeneic HCT, who had worse outcomes than those without occult pathogen detection.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e Identifying a potential role of HHV-6B in causing or contributing to LRTD is of significant clinical interest given the ready availability of diagnostic tests and antiviral therapies.\u003c/p\u003e \u003cp\u003eIn the current study, we found that 37% of BAL events had detectable HHV-6B DNA. The observation of generally higher viral loads and more frequent detection of HHV-6B in BALs obtained earlier after HCT are consistent with the epidemiology of HHV-6B reactivation and HHV-6B-encephalitis after HCT.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e Detection of HHV-6B in BALF in the absence of concurrent detection in the plasma also points to independent, compartmentalized reactivation of HHV-6B within the lung parenchyma.\u003c/p\u003e \u003cp\u003eA unique strength of this study was prospective collection and processing of BALF to allow testing for mRNA gene transcripts. We used targeted RT-qPCR to test for two HHV-6B-specific transcripts, U38 (a late DNA polymerase gene) and U90 (an immediate early gene), which have high sensitivity and specificity for viral replication in the context of lytic infection.\u003csup\u003e\u003cspan additionalcitationids=\"CR53 CR54 CR55\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e We found that HHV-6B DNA viral loads\u0026thinsp;\u0026ge;\u0026thinsp;2.3 and \u0026ge;\u0026thinsp;2.8 log\u003csub\u003e10\u003c/sub\u003e copies/mL were highly predictive of detecting one or both mRNA transcripts, respectively. This evidence suggests that these thresholds may distinguish between viral shedding and lytic infection to reduce the \u0026lsquo;signal-to-noise\u0026rsquo; ratio when testing cellular samples like BALF. Strikingly, these values are very similar to the HHV-6B DNA viral load threshold of 2.5 log\u003csub\u003e10\u003c/sub\u003e copies/mL identified in our aforementioned retrospective study, in which viral loads above this level were associated with mortality and death from respiratory failure with effect sizes similar to the current study.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e Further evidence of tissue-invasive disease was demonstrated in our prior study using RNA \u003cem\u003ein situ\u003c/em\u003e hybridization to detect HHV-6 gene expression in lung tissue from three patients with HHV-6B DNA in the BALF. Similar findings have been demonstrated for CMV in a study of allogeneic HCT recipients, wherein BALF CMV\u0026thinsp;\u0026gt;\u0026thinsp;2.7 log\u003csub\u003e10\u003c/sub\u003e copies/mL (500 copies/mL) distinguished between CMV pneumonia and viral shedding without CMV pneumonia.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e Additionally, that study demonstrated a distribution of CMV detection across LRTD categories similar to that observed for HHV-6B in the current analysis.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eHost gene expression profiling is increasingly reported as a highly discriminatory technique for pathogen-associated disease assessment.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e Pathogens trigger specific host transcriptional programs by interacting with pattern recognition receptors on leukocytes and other cells to mediate immune responses. Thus, unique transcriptional signatures may be defined to discriminate causes of LRTD, and this approach has been used with blood and BALF for a range of pathogens in other clinical contexts.\u003csup\u003e\u003cspan additionalcitationids=\"CR38 CR39 CR40 CR41 CR42 CR43\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e One study identified primary HHV-6 infection in healthy children as a driver of interferon and toll-like receptor signaling,\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e but studies in HCT recipients are lacking. In this study, we identified differential up- and down-regulation of whole blood gene expression pathways among patients with versus without HHV-6B detection in BALF. Notably, HHV-6B detection in participants with clinically diagnosed IPS and fungal LRTD was associated with a strong host interferon response and enrichment in a variety of pathways consistent with active viral infection, implicating HHV-6B as an independent but unrecognized cause of pneumonitis. Further support for the role of HHV-6B detection in the lungs as an active and distinct infection in participants clinically diagnosed with IPS was our observation through PCA that samples from patients with HHV-6B detection in BALF (but not plasma) clustered with samples from patients who had only plasma detection\u0026mdash;and both groups segregated from HHV-6B negative samples.\u003c/p\u003e \u003cp\u003eStrengths of this study include prospective enrollment and collection of paired BALF and plasma samples from a large cohort of contemporary HCT recipients at three centers. This is the first study to preserve BALF for RNA analysis to examine key insights into the correlation between HHV-6B DNA detection and viral transcription in HCT patients with LRTD. Because isolation and testing of mRNA is labor-intensive, not routinely available, and currently infeasible in a clinical setting, identification of a relevant genomic DNA threshold (for which testing is routinely available) is critical for clinical translation. Paired whole blood samples used for gene expression profiling generated additional observations in support of HHV-6B as a pulmonary pathogen and pave the way for similar approaches to disease definition in the HCT population. Although the transcriptional signal associated with HHV-6B detection was weaker in the fungal LRTD subgroup, GSEA\u0026rsquo;s statistical model allowed identification of significantly enriched pathways even when gene-specific differences were modest. This study also has limitations. There may be residual confounding in the analyses given the observational design, as well as the possibility of misclassification of LRTD categories based on clinical evaluation. Absence of HHV-6B mRNA detection in BALF could be due to variability in mRNA preservation, which could limit interpretation of the ROC analysis; nonetheless, the identified DNA viral load thresholds had clinical significance in the adjusted models. Gene expression analyses were limited by small sample sizes when stratified by HHV-6B detection and clinical disease subgroups precluding adjustment for other variables that could affect the results. While a randomized controlled prevention or treatment trial would provide the most definitive evidence for HHV-6B as a pulmonary pathogen, such trials would be very difficult, if not impossible, to conduct given the availability of effective antiviral agents and the sample sizes needed. Also, this level of evidence also does not exist for most microorganisms that are widely accepted as pulmonary pathogens after transplantation. Absent this, data from the current study and prior work support HHV-6B as a co-pathogen or primary pathogen in LRTD after allogeneic HCT. Considering commercial availability of similarly sensitive HHV-6 quantitative PCR assays and antiviral treatments (e.g., ganciclovir and foscarnet), there is opportunity to identify and treat affected patients.\u003c/p\u003e \u003cp\u003eIn conclusion, we used multiple molecular diagnostic techniques coupled with clinical observations to provide evidence that HHV-6B reactivation in the lungs after allogeneic HCT has immunologically distinct effects on host gene expression and measurable impact on mortality risk. Higher viral load correlated with detection of HHV-6B gene expression to suggest lytic infection and supported validation of viral load thresholds associated with clinical outcomes. Data from this study fulfill contemporary molecular guidelines for establishing disease causation,\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e and recognition of HHV-6B as a pulmonary pathogen has the potential for therapeutic targeting and improvement of outcomes after allogeneic HCT.\u003c/p\u003e"},{"header":"ONLINE METHODS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003eParticipants\u003c/h2\u003e\n\u003cp\u003eWe prospectively enrolled adult allogeneic HCT recipients undergoing a BAL for evaluation of LRTD within 120 days of HCT from Fred Hutchinson Cancer Center (Fred Hutch), City of Hope Comprehensive Cancer Center (COH), and Memorial Sloan Kettering Cancer Center (MSKCC). Data were abstracted from medical records for participant characteristics, microbiologic results, and outcomes for up to 60 days after the BAL date. The study was approved by the Fred Hutch Institutional Review Board and participants provided written informed consent.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003eLRTD Categorization\u003c/h2\u003e\n\u003cp\u003eWe defined LRTD as any abnormal radiographic findings with lower respiratory tract symptoms (e.g., cough, shortness of breath, or decreased oxygen saturation). Bacterial, fungal, and viral causes were defined according to consensus guidelines.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e Idiopathic pneumonia syndrome (IPS), in which no microbiologically documented infection was detected or suspected, was defined according to the American Thoracic Society guideline.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e Diffuse alveolar hemorrhage (DAH) was defined as increasingly bloody return from serial lavages or report of \u0026ldquo;bloody\u0026rdquo; BALF followed by treatment with corticosteroids.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e If a microbiologic or non-infectious (i.e., idiopathic pneumonia syndrome [IPS]) cause of LRTD was not determined based on a standardized review of microbiologic, histopathologic, and other diagnostic criteria, we categorized the episode as \u0026lsquo;other\u0026rsquo;. All infectious and non-infectious causes of LRTD were based on review of clinical, microbiologic, histopathologic, and radiographic records. Final determinations were made by author J.A.H.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch2\u003eMicrobiologic testing\u003c/h2\u003e\n\u003cp\u003eThe following tests were routinely performed on BALF throughout the study period: gram stain, fungal stain, and acid fast bacilli stain; cytology examination; conventional cultures for bacteria, fungi, mycobacteria, nocardia, and legionella; shell viral centrifugation viral cultures for CMV and/or PCR; and direct fluorescent antibody (DFA) testing and/or PCR for \u003cem\u003ePneumocystis jirovecii\u003c/em\u003e. Serum and BALF were routinely tested for \u003cem\u003eAspergillus\u003c/em\u003e with the galactomannan index (GMI) using the Bio-Rad Platelia Assay (Hercules, CA).\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e A GMI\u0026thinsp;\u0026ge;\u0026thinsp;0.5 with a confirmatory index processed separately on the same sample was considered positive for both BALF and serum. Additionally, multiplex quantitative reverse-transcription PCR panel was used to detect up to 12 respiratory viruses including influenza A, influenza B, RSV, PIV types 1 to 4, adenovirus, human metapneumovirus, coronavirus, rhinovirus, and bocavirus.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e At MSKCC, the BioFire FilmArray Respiratory Panel (Salt Lake City, Utah) was used to detect up to 14 respiratory viruses in addition to \u003cem\u003eBordetella pertussis\u003c/em\u003e, \u003cem\u003eChlamydophila pneumoniae\u003c/em\u003e, and \u003cem\u003eMycoplasma pneumoniae.\u003c/em\u003e\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003eProcedures, Samples, and Testing\u003c/h2\u003e\n\u003cp\u003eAt all sites, eligible patients routinely underwent a BAL, if clinically appropriate, using standard procedures based on international consensus guidelines.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e Patients were not routinely tested or treated for HHV-6B in blood or BALF during the study period.\u003c/p\u003e\n\u003cp\u003eUp to 15 mL of BALF were obtained in real-time during the bronchoscopy and immediately processed. 0.25 mL of fresh BALF were aliquoted and frozen at -80\u0026deg; Celsius. Using a minimum of 2 mL and up to 10 mL of BALF, a cell pellet was isolated, resuspended in RNAprotect Cell Reagent (QIAGEN, Germantown, MD), and stored at 4\u0026deg; Celsius for at least 12 hours and up to 72 hours) before storing at -80\u0026deg; Celsius. At the time of the BAL procedure, 6 mL of blood were collected in EDTA tubes for plasma isolation and 2.5 mL of blood in PAXgene tubes (QIAGEN, Germantown, MD). PAXgene tubes were stored according to the package insert.\u003c/p\u003e\n\u003cp\u003eWe extracted DNA from fresh BALF aliquots and plasma for HHV-6B testing using real-time quantitative polymerase chain reaction (qPCR) assays as previously described.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e The assay has a lower limit of detection (LLD) of 10 copies/mL and a lower limit of quantitation (LLQ) of 50 copies/mL; a threshold of \u0026ge;\u0026thinsp;50 copies/mL was considered positive. We performed qPCR for the human \u0026beta;-globin gene from BALF to confirm cellularity and the quality of DNA extraction.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eWe extracted RNA from BALF cell pellets using the RNAeasy Mini Kit (QIAGEN, Germantown, MD). We extracted RNA from whole blood stored in PAXgene tubes using the MagMAX\u0026trade; for Stabilized Blood Tubes RNA Isolation Kit and GLOBINclear (Thermo Fisher Scientific, Bothell, WA) to deplete alpha and beta globin mRNA from whole blood samples according to the package inserts. DNase treatment was incorporated into both BAL and blood RNA extractions to reduce contamination by genomic DNA.\u003c/p\u003e\n\u003cp\u003eWe separately tested aliquots of 5 \u0026micro;L of eluted RNA from BAL and blood samples for two HHV-6B mRNA gene transcripts, U38 (late DNA polymerase gene) and U90 (immediate early gene), with a laboratory developed reverse transcription qPCR (RT-qPCR) assay.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e Synthetically produced RNA transcripts were used for standards. Intra and inter-assay reproducibility were tested, and amplification of house-keeping genes were performed as controls. These targets were previously demonstrated to have high sensitivity and specificity for viral replication.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e We also performed RT-qPCR for GAPDH to confirm the quality of RNA extraction. RNA concentration was assessed using Nanodrop (Thermo Fisher Scientific, Bothell, WA).\u003c/p\u003e\n\u003cp\u003eRemaining RNA extracted from whole blood samples was processed in the Fred Hutch Genomics \u0026amp; Bioinformatics Shared Resource. We used the TruSeq RNA Exome, previously known as the TruSeq RNA Access kit (Illumina, San Diego, CA), to prepare cDNA libraries. RNA purity was assessed with an Agilent 4200 TapeStation (Agilent Technologies, Santa Clara, CA, USA). Additional library QC, blending of pooled indexed libraries, and cluster optimization was performed using Life Technologies\u0026rsquo; Invitrogen Qubit\u0026reg; 2.0 Fluorometer (Life Technologies-Invitrogen, Carlsbad, CA, USA). Barcoded RNAseq libraries were pooled (54-plexed) and clustered onto an S1 flow cell. Sequencing was performed using an Illumina NovaSeq 6000 employing a paired-end, 50 base read length (PE50) sequencing strategy. Basecalling and demultiplexing of barcoded reads were performed with Illumina bcl2fastq v2.20. Read quality was summarized using FastQC 0.11.9. Paired reads were aligned to the UCSC GRCh38/hg38 human reference genome using STAR 2.7.7a.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e Annotations from Gencode v38 were provided with the \u0026ldquo;--quantMode GeneCounts\u0026rdquo; option to generate per-gene counts during the alignment process. Only annotations for protein coding genes, polymorphic pseudogenes, and lncRNAs were considered. The second-strand read counts for each gene were combined into a single gene-count matrix for all samples.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\n\u003cp\u003eWe described the distribution of demographic and clinical characteristics between patients with and without any HHV-6B detection in BALF, as well as the sensitivity and specificity of HHV-6B DNA detection in plasma for predicting HHV-6B DNA detection in BALF. We depicted the distribution of HHV-6B DNA and mRNA detection in BALF by time post-HCT and LRTD category. We generated receiver operating characteristic (ROC) curves to determine the ability of quantitative BALF HHV-6B DNA values to predict HHV-6B mRNA detection.\u003c/p\u003e\n\u003cp\u003eWe used the observed BALF HHV-6B DNA threshold from ROC curves that optimally predicted HHV-6B mRNA detection to define cases of possible HHV-6B pneumonitis. The results from HHV-6B DNA detection were used instead of mRNA detection to improve generalizability for potential clinical applications, as HHV-6 DNA testing is readily available whereas testing for mRNA is not. We generated Kaplan-Meier and cumulative incidence curves to estimate overall mortality and death due to respiratory causes (i.e., \u0026lsquo;pulmonary death\u0026rsquo;), respectively, by 30 and 60 days after BAL in patients with and without possible HHV-6B pneumonitis, irrespective of other causes of LRTD. In the cumulative incidence curves, non-pulmonary deaths were competing events. Univariate and multivariate Cox proportional hazards models were used to compare these outcomes by day 60 after the BAL event, adjusted for potential confounders, including other causes of LRTD. Two-sided P values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant. SAS version 9.3 (SAS institute, Cary, NC) was used for analyses.\u003c/p\u003e\n\u003cp\u003eFor RNA-Seq analyses, we identified subgroups of participants who had a well-documented clinically determined single cause of LRTD (i.e., bacterial, viral, fungal, or IPS). In each diagnostic category, we compared whole-transcriptome expression variability between patients with and without possible HHV-6B pneumonitis using Principal Component Analysis (PCA). We identified differentially expressed genes using the R package DESeq2.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e Significant differential gene expression was based on a |log2 (ratio)| \u0026ge; 0.585 (\u0026plusmn;\u0026thinsp;1.5-fold) difference and a false discovery rate (FDR) set to 5% to correct for multiple testing.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e To identify the type of transcriptional programs activated in a given sample, we applied Gene Set Enrichment Analysis (GSEA)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e using 50 Hallmark and 1329 Canonical Pathway gene sets curated from the Molecular Signature Database (MSigDB)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e with a FDR threshold of 5% to denote significantly enriched pathways. Differentially up or down regulated gene sets were graphed based on their FDR and normalized enrichment scores using volcano plots.\u003c/p\u003e\n\u003cp\u003eWe performed \u003cem\u003ea priori\u003c/em\u003e sample size calculations to estimate target sample sizes and power for detecting differences in overall mortality and gene expression based on preliminary data. We estimated that with a target sample size of approximately 150, we would identify up to 48 participants with any HHV-6B detection in BALF, about half of whom would have detectable HHV-6B mRNA transcripts (n\u0026thinsp;=\u0026thinsp;24). To test the hypothesis that patients with possible HHV-6B LRTD will have higher mortality than patients without, we estimated that these sample sizes would be able to demonstrate a statistically significant difference if the hazard ratio was at least 2.5\u0026ndash;2.6 for effect sizes ranging from 5\u0026ndash;20%, with 80% power and a two-sided type I error rate of 0.05. For the subgroup analyses with RNA-Seq, we estimated identifying 10\u0026ndash;15 participants per LRTD category who met the described criteria. A power analysis for 2-group comparisons using the RNA-seq Power R package\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e with parameters estimated from a representative RNA-seq dataset demonstrated that the study would be adequately powered (84%-95%) to detect meaningful changes in gene expression with \u0026ge;\u0026thinsp;1.75-2 fold changes assuming a FDR of 5%.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrior presentation of the data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was presented in part at IDWeek 2022 in Washington, DC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial support\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Institutes of Health [5K23AI119133 to J.A.H. and K24HL093294 to M.B.] and the American Society for Transplantation and Cellular Therapy/National Marrow Donor Program/Be The Match Foundation [Amy Strelzer Manasevit Research Program] to J.A.H. Additional resources were provided by the National Institutes of Health Fred Hutch/University of Washington/Seattle Children\u0026rsquo;s Cancer Consortium grant [P30 CA015704] and the Fred Hutch Genomics \u0026amp; Bioinformatics Shared Resource.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePotential conflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJAH: Consulting for Allovir, Gilead, Karius, Symbio; research support from Allovir, Gilead, Karius, Merck.\u003c/p\u003e\n\u003cp\u003eYJL: Research support from Karius, AiCuris, Scynexis, and Merck \u0026amp; Co Inc.\u003c/p\u003e\n\u003cp\u003eAW: Consulting for Kyorin Pharmaceutical; research support from Allovir, Ansun Biopharma, Pfizer, Vir/GSK.\u003c/p\u003e\n\u003cp\u003eGRH:\u0026nbsp;Consulting for Generon Corporation, NapaJen Pharma, iTeos Therapeutics, Neoleukin Therapeutics, Commonwealth Serum Laboratories, Cynata Therapeutics; research support from Compass Therapeutics, Syndax Pharmaceuticals, Applied Molecular Transport, Serplus Technology, Heat Biologics, Laevoroc Oncology, iTEOS therapetics and Genentech.\u003c/p\u003e\n\u003cp\u003eDMZ: Consulting for Allovir.\u003c/p\u003e\n\u003cp\u003eMB: Consulting for Allovir, Symbio, Evrys Bio; research support from Merck.\u003c/p\u003e\n\u003cp\u003eAll other authors report no disclosures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge Jeffrey Delrow, Matt Fitzgibbon, Alyssa Dawson, and Pamela Yang for their help with bioinformatics and sample processing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ.A.H., L.K.V-V., D.M.Z., A.W., W.M.L., G-S.C., G.H., and M.B. were responsible for the design of the study and/or interpretation of the data. J.A.H., H.X., W.M.L., and S.A.G. analyzed the data and created the figures. Data were collected by J.A.H., Y.J.L., L.K.V-V., E.L.C., and S.D. Laboratory work was carried out or supervised by M-L.H., H.Z., and K.R.J. J.A.H wrote the first draft. All authors contributed to the writing and revision of the manuscript and approved the final version.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAguilar-Guisado, M. \u003cem\u003eet al.\u003c/em\u003e Pneumonia in allogeneic stem cell transplantation recipients: a multicenter prospective study. \u003cem\u003eClin Transplant\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, E629-38 (2011).\u003c/li\u003e\n\u003cli\u003ePanoskaltsis-Mortari, A. \u003cem\u003eet al.\u003c/em\u003e An official American Thoracic Society research statement: noninfectious lung injury after hematopoietic stem cell transplantation: idiopathic pneumonia syndrome. \u003cem\u003eAm J Respir Crit Care Med\u003c/em\u003e \u003cstrong\u003e183\u003c/strong\u003e, 1262\u0026ndash;79 (2011).\u003c/li\u003e\n\u003cli\u003eYen, K. T., Lee, A. S., Krowka, M. J. \u0026amp; Burger, C. D. Pulmonary complications in bone marrow transplantation: a practical approach to diagnosis and treatment. \u003cem\u003eClin Chest Med\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 189\u0026ndash;201 (2004).\u003c/li\u003e\n\u003cli\u003eGooley, T. A. \u003cem\u003eet al.\u003c/em\u003e Reduced mortality after allogeneic hematopoietic-cell transplantation. \u003cem\u003eN Engl J Med\u003c/em\u003e \u003cstrong\u003e363\u003c/strong\u003e, 2091\u0026ndash;101 (2010).\u003c/li\u003e\n\u003cli\u003eFukuda, T. \u003cem\u003eet al.\u003c/em\u003e Risks and outcomes of idiopathic pneumonia syndrome after nonmyeloablative and conventional conditioning regimens for allogeneic hematopoietic stem cell transplantation. \u003cem\u003eBlood\u003c/em\u003e \u003cstrong\u003e102\u003c/strong\u003e, 2777\u0026ndash;85 (2003).\u003c/li\u003e\n\u003cli\u003eSeo, S. \u003cem\u003eet al.\u003c/em\u003e Idiopathic pneumonia syndrome after hematopoietic cell transplantation: Evidence of occult infectious etiologies. \u003cem\u003eBlood\u003c/em\u003e (2015) doi:10.1182/blood-2014-12-617035.\u003c/li\u003e\n\u003cli\u003eWenger, D. S. \u003cem\u003eet al.\u003c/em\u003e Incidence, Risk Factors, and Outcomes of Idiopathic Pneumonia Syndrome after Allogeneic Hematopoietic Cell Transplantation. \u003cem\u003eBiol Blood Marrow Transplant\u003c/em\u003e \u003cstrong\u003e26\u003c/strong\u003e, 413\u0026ndash;420 (2020).\u003c/li\u003e\n\u003cli\u003eEnglund, J. A. \u003cem\u003eet al.\u003c/em\u003e Brief communication: fatal human metapneumovirus infection in stem-cell transplant recipients. \u003cem\u003eAnn Intern Med\u003c/em\u003e \u003cstrong\u003e144\u003c/strong\u003e, 344\u0026ndash;349 (2006).\u003c/li\u003e\n\u003cli\u003eCone, R. W. \u003cem\u003eet al.\u003c/em\u003e Human herpesvirus 6 in lung tissue from patients with pneumonitis after bone marrow transplantation. \u003cem\u003eN Engl J Med\u003c/em\u003e \u003cstrong\u003e329\u003c/strong\u003e, 156\u0026ndash;61 (1993).\u003c/li\u003e\n\u003cli\u003eBuchbinder, S., Elmaagacli, A. H., Schaefer, U. W. \u0026amp; Roggendorf, M. Human herpesvirus 6 is an important pathogen in infectious lung disease after allogeneic bone marrow transplantation. \u003cem\u003eBone Marrow Transplant\u003c/em\u003e \u003cstrong\u003e26\u003c/strong\u003e, 639\u0026ndash;44 (2000).\u003c/li\u003e\n\u003cli\u003eNagate et al. Detection and quantification of human herpesvirus 6 genomes using bronchoalveolar lavage fluid in immunocompromised patients with interstitial pneumonia. \u003cem\u003eInt J Mol Med\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 379 (2001).\u003c/li\u003e\n\u003cli\u003eNishimaki, K. \u003cem\u003eet al.\u003c/em\u003e The possible involvement of human herpesvirus type 6 in obliterative bronchiolitis after bone marrow transplantation. \u003cem\u003eBone Marrow Transplant\u003c/em\u003e \u003cstrong\u003e32\u003c/strong\u003e, 1103\u0026ndash;5 (2003).\u003c/li\u003e\n\u003cli\u003eCosta, C. \u003cem\u003eet al.\u003c/em\u003e Quantitative detection of HHV-6 and HHV-7 in transbronchial biopsies from lung transplant recipients. \u003cem\u003eNew Microbiol\u003c/em\u003e \u003cstrong\u003e34\u003c/strong\u003e, 275\u0026ndash;80 (2011).\u003c/li\u003e\n\u003cli\u003eLautenschlager, I. \u0026amp; Razonable, R. R. Human herpesvirus-6 infections in kidney, liver, lung, and heart transplantation: review. \u003cem\u003eTranspl Int\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 493\u0026ndash;502 (2012).\u003c/li\u003e\n\u003cli\u003eCarrigan, D. R. \u003cem\u003eet al.\u003c/em\u003e Interstitial pneumonitis associated with human herpesvirus-6 infection after marrow transplantation. \u003cem\u003eLancet\u003c/em\u003e \u003cstrong\u003e338\u003c/strong\u003e, 147\u0026ndash;9 (1991).\u003c/li\u003e\n\u003cli\u003eMariotte, E. \u003cem\u003eet al.\u003c/em\u003e Significance of herpesvirus 6 in BAL fluid of hematology patients with acute respiratory failure. \u003cem\u003eInfection\u003c/em\u003e \u003cstrong\u003e39\u003c/strong\u003e, 225\u0026ndash;30 (2011).\u003c/li\u003e\n\u003cli\u003eZerr, D. M. \u003cem\u003eet al.\u003c/em\u003e A population-based study of primary human herpesvirus 6 infection. \u003cem\u003eN Engl J Med\u003c/em\u003e \u003cstrong\u003e352\u003c/strong\u003e, 768\u0026ndash;76 (2005).\u003c/li\u003e\n\u003cli\u003eKrueger, G. R. \u003cem\u003eet al.\u003c/em\u003e Latent herpesvirus-6 in salivary and bronchial glands. \u003cem\u003eLancet\u003c/em\u003e \u003cstrong\u003e336\u003c/strong\u003e, 1255\u0026ndash;6 (1990).\u003c/li\u003e\n\u003cli\u003eDe Bolle, L., Naesens, L. \u0026amp; De Clercq, E. Update on human herpesvirus 6 biology, clinical features, and therapy. \u003cem\u003eClin Microbiol Rev\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, 217\u0026ndash;45 (2005).\u003c/li\u003e\n\u003cli\u003eNoviello, M. \u003cem\u003eet al.\u003c/em\u003e Human herpesvirus 6-specific T-cell immunity in allogeneic hematopoietic stem cell transplant recipients. \u003cem\u003eBlood Adv\u003c/em\u003e (2023) doi:10.1182/BLOODADVANCES.2022009274.\u003c/li\u003e\n\u003cli\u003eHill, J. A. \u003cem\u003eet al.\u003c/em\u003e Kinetics of Double Stranded DNA Viremia after Allogeneic Hematopoietic Cell Transplantation. \u003cem\u003eClin Infect Dis\u003c/em\u003e (2017) doi:10.1093/cid/cix804.\u003c/li\u003e\n\u003cli\u003eSeo, S. \u003cem\u003eet al.\u003c/em\u003e Idiopathic pneumonia syndrome after hematopoietic cell transplantation: evidence of occult infectious etiologies. \u003cem\u003eBlood\u003c/em\u003e \u003cstrong\u003e125\u003c/strong\u003e, 3789\u0026ndash;97 (2015).\u003c/li\u003e\n\u003cli\u003eHill, J. A. \u003cem\u003eet al.\u003c/em\u003e Human herpesvirus 6B and lower respiratory tract disease after hematopoietic cell transplantation. \u003cem\u003eJournal of Clinical Oncology\u003c/em\u003e \u003cstrong\u003e37\u003c/strong\u003e, (2019).\u003c/li\u003e\n\u003cli\u003eTomblyn, M. \u003cem\u003eet al.\u003c/em\u003e Guidelines for preventing infectious complications among hematopoietic cell transplantation recipients: a global perspective. \u003cem\u003eBiol Blood Marrow Transplant\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 1143\u0026ndash;238 (2009).\u003c/li\u003e\n\u003cli\u003eChemaly, R. F., Hill, J. A., Voigt, S. \u0026amp; Peggs, K. S. In vitro comparison of currently available and investigational antiviral agents against pathogenic human double-stranded DNA viruses: A systematic literature review. \u003cem\u003eAntiviral Res\u003c/em\u003e \u003cstrong\u003e163\u003c/strong\u003e, (2019).\u003c/li\u003e\n\u003cli\u003eChellapandian, D. \u003cem\u003eet al.\u003c/em\u003e Bronchoalveolar lavage and lung biopsy in patients with cancer and hematopoietic stem-cell transplantation recipients: a systematic review and meta-analysis. \u003cem\u003eJ Clin Oncol\u003c/em\u003e \u003cstrong\u003e33\u003c/strong\u003e, 501\u0026ndash;9 (2015).\u003c/li\u003e\n\u003cli\u003eBoeckh, M. \u003cem\u003eet al.\u003c/em\u003e Cytomegalovirus (CMV) DNA Quantitation in Bronchoalveolar Lavage Fluid from Hematopoietic Stem Cell Transplant Recipients with CMV Pneumonia. \u003cem\u003eJournal of Infectious Diseases\u003c/em\u003e \u003cstrong\u003e215\u003c/strong\u003e, 1514\u0026ndash;1522 (2017).\u003c/li\u003e\n\u003cli\u003eBoivin, G. \u003cem\u003eet al.\u003c/em\u003e Quantitation of cytomegalovirus DNA and characterization of viral gene expression in bronchoalveolar cells of infected patients with and without pneumonitis. \u003cem\u003eJ Infect Dis\u003c/em\u003e \u003cstrong\u003e173\u003c/strong\u003e, 1304\u0026ndash;12 (1996).\u003c/li\u003e\n\u003cli\u003eTorres, J. \u003cem\u003eet al.\u003c/em\u003e Application of Host Genome-wide Expression Profiles for Early Identification of Sepsis in Children with Cancer, Fever and Neutropenia at High Risk of Invasive Bacterial Infections. \u003cem\u003eIDWeek Abstract\u003c/em\u003e 1459 (2012).\u003c/li\u003e\n\u003cli\u003eHu, X., Yu, J., Crosby, S. D. \u0026amp; Storch, G. A. Gene expression profiles in febrile children with defined viral and bacterial infection. \u003cem\u003eProc Natl Acad Sci U S A\u003c/em\u003e \u003cstrong\u003e110\u003c/strong\u003e, 12792\u0026ndash;7 (2013).\u003c/li\u003e\n\u003cli\u003eRamilo, O. \u0026amp; Mej\u0026iacute;as, A. Shifting the paradigm: host gene signatures for diagnosis of infectious diseases. \u003cem\u003eCell Host Microbe\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 199\u0026ndash;200 (2009).\u003c/li\u003e\n\u003cli\u003eZinter, M. S. \u003cem\u003eet al.\u003c/em\u003e The pulmonary metatranscriptome prior to pediatric HCT identifies post-HCT lung injury. \u003cem\u003eBlood\u003c/em\u003e \u003cstrong\u003e137\u003c/strong\u003e, 1679\u0026ndash;1689 (2021).\u003c/li\u003e\n\u003cli\u003eZinter, M. S. \u003cem\u003eet al.\u003c/em\u003e Pulmonary microbiome and gene expression signatures differentiate lung function in pediatric hematopoietic cell transplant candidates. \u003cem\u003eSci Transl Med\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, (2022).\u003c/li\u003e\n\u003cli\u003eRamilo, O. \u003cem\u003eet al.\u003c/em\u003e Gene expression patterns in blood leukocytes discriminate patients with acute infections. \u003cem\u003eBlood\u003c/em\u003e \u003cstrong\u003e109\u003c/strong\u003e, 2066\u0026ndash;2077 (2007).\u003c/li\u003e\n\u003cli\u003eMcClain, M. T. \u003cem\u003eet al.\u003c/em\u003e A blood-based host gene expression assay for early detection of respiratory viral infection: an index-cluster prospective cohort study. \u003cem\u003eLancet Infect Dis\u003c/em\u003e \u003cstrong\u003e21\u003c/strong\u003e, 396\u0026ndash;404 (2021).\u003c/li\u003e\n\u003cli\u003eSuarez, N. M. \u003cem\u003eet al.\u003c/em\u003e Superiority of transcriptional profiling over procalcitonin for distinguishing bacterial from viral lower respiratory tract infections in hospitalized adults. \u003cem\u003eJ Infect Dis\u003c/em\u003e \u003cstrong\u003e212\u003c/strong\u003e, 213\u0026ndash;222 (2015).\u003c/li\u003e\n\u003cli\u003eMeyer, K. C. Bronchoalveolar lavage as a diagnostic tool. \u003cem\u003eSemin Respir Crit Care Med\u003c/em\u003e \u003cstrong\u003e28\u003c/strong\u003e, 546\u0026ndash;60 (2007).\u003c/li\u003e\n\u003cli\u003eRaju, B. \u003cem\u003eet al.\u003c/em\u003e Gene expression profiles of bronchoalveolar cells in pulmonary TB. \u003cem\u003eTuberculosis (Edinb)\u003c/em\u003e \u003cstrong\u003e88\u003c/strong\u003e, 39\u0026ndash;51 (2008).\u003c/li\u003e\n\u003cli\u003eColvin, J. M. \u003cem\u003eet al.\u003c/em\u003e Detection of viruses in young children with fever without an apparent source. \u003cem\u003ePediatrics\u003c/em\u003e \u003cstrong\u003e130\u003c/strong\u003e, e1455-62 (2012).\u003c/li\u003e\n\u003cli\u003eZaas, A. K. \u003cem\u003eet al.\u003c/em\u003e Gene expression signatures diagnose influenza and other symptomatic respiratory viral infections in humans. \u003cem\u003eCell Host Microbe\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 207\u0026ndash;17 (2009).\u003c/li\u003e\n\u003cli\u003ePopper, S. J. \u003cem\u003eet al.\u003c/em\u003e Gene transcript abundance profiles distinguish Kawasaki disease from adenovirus infection. \u003cem\u003eJ Infect Dis\u003c/em\u003e \u003cstrong\u003e200\u003c/strong\u003e, 657\u0026ndash;66 (2009).\u003c/li\u003e\n\u003cli\u003eRamilo, O. \u003cem\u003eet al.\u003c/em\u003e Gene expression patterns in blood leukocytes discriminate patients with acute infections. \u003cem\u003eBlood\u003c/em\u003e \u003cstrong\u003e109\u003c/strong\u003e, 2066\u0026ndash;77 (2007).\u003c/li\u003e\n\u003cli\u003eStojanov, S. \u003cem\u003eet al.\u003c/em\u003e Periodic fever, aphthous stomatitis, pharyngitis, and adenitis (PFAPA) is a disorder of innate immunity and Th1 activation responsive to IL-1 blockade. \u003cem\u003eProc Natl Acad Sci U S A\u003c/em\u003e \u003cstrong\u003e108\u003c/strong\u003e, 7148\u0026ndash;53 (2011).\u003c/li\u003e\n\u003cli\u003eThompson, M. R., Kaminski, J. J., Kurt-Jones, E. A. \u0026amp; Fitzgerald, K. A. Pattern recognition receptors and the innate immune response to viral infection. \u003cem\u003eViruses\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, 920\u0026ndash;40 (2011).\u003c/li\u003e\n\u003cli\u003eBaron, E. J. \u003cem\u003eet al.\u003c/em\u003e A guide to utilization of the microbiology laboratory for diagnosis of infectious diseases: 2013 recommendations by the Infectious Diseases Society of America (IDSA) and the American Society for Microbiology (ASM)(a). \u003cem\u003eClin Infect Dis\u003c/em\u003e \u003cstrong\u003e57\u003c/strong\u003e, e22\u0026ndash;e121 (2013).\u003c/li\u003e\n\u003cli\u003eDe Pauw, B. \u003cem\u003eet al.\u003c/em\u003e Revised definitions of invasive fungal disease from the European Organization for Research and Treatment of Cancer/Invasive Fungal Infections Cooperative Group and the National Institute of Allergy and Infectious Diseases Mycoses Study Group (EORTC/MSG) C. \u003cem\u003eClin Infect Dis\u003c/em\u003e \u003cstrong\u003e46\u003c/strong\u003e, 1813\u0026ndash;21 (2008).\u003c/li\u003e\n\u003cli\u003eAscioglu, S. \u003cem\u003eet al.\u003c/em\u003e Defining opportunistic invasive fungal infections in immunocompromised patients with cancer and hematopoietic stem cell transplants: an international consensus. \u003cem\u003eClin Infect Dis\u003c/em\u003e \u003cstrong\u003e34\u003c/strong\u003e, 7\u0026ndash;14 (2002).\u003c/li\u003e\n\u003cli\u003ePanoskaltsis-Mortari, A. \u003cem\u003eet al.\u003c/em\u003e An official American Thoracic Society research statement: noninfectious lung injury after hematopoietic stem cell transplantation: idiopathic pneumonia syndrome. \u003cem\u003eAm J Respir Crit Care Med\u003c/em\u003e \u003cstrong\u003e183\u003c/strong\u003e, 1262\u0026ndash;79 (2011).\u003c/li\u003e\n\u003cli\u003eToomey, D., Phan, T. L., Phan, T., Hill, J. A. \u0026amp; Zerr, D. M. Viral Encephalitis after Hematopoietic Cell Transplantation: A Systematic Review. \u003cem\u003eTransplant Cell Ther\u003c/em\u003e (2023) doi:10.1016/J.JTCT.2023.06.022.\u003c/li\u003e\n\u003cli\u003eErard, V. \u003cem\u003eet al.\u003c/em\u003e Reduced Mortality of Cytomegalovirus Pneumonia After Hematopoietic Cell Transplantation Due to Antiviral Therapy and Changes in Transplantation Practices. \u003cem\u003eClinical Infectious Diseases\u003c/em\u003e \u003cstrong\u003e61\u003c/strong\u003e, 31\u0026ndash;39 (2015).\u003c/li\u003e\n\u003cli\u003eZhou, X. \u003cem\u003eet al.\u003c/em\u003e First Onset Herpesviral Infection and Lung Injury in Allogeneic Hematopoietic Cell Transplantation. \u003cem\u003eAm J Respir Crit Care Med\u003c/em\u003e rccm.201809-1635OC (2019) doi:10.1164/rccm.201809-1635OC.\u003c/li\u003e\n\u003cli\u003eBressollette-Bodin, C. \u003cem\u003eet al.\u003c/em\u003e Quantification of two viral transcripts by real time PCR to investigate human herpesvirus type 6 active infection. \u003cem\u003eJ Clin Virol\u003c/em\u003e \u003cstrong\u003e59\u003c/strong\u003e, 94\u0026ndash;9 (2014).\u003c/li\u003e\n\u003cli\u003eIhira, M. \u003cem\u003eet al.\u003c/em\u003e Development of quantitative RT-PCR assays for detection of three classes of HHV-6B gene transcripts. \u003cem\u003eJ Med Virol\u003c/em\u003e \u003cstrong\u003e84\u003c/strong\u003e, 1388\u0026ndash;95 (2012).\u003c/li\u003e\n\u003cli\u003eVan den Bosch, G. \u003cem\u003eet al.\u003c/em\u003e Development of reverse transcriptase PCR assays for detection of active human herpesvirus 6 infection. \u003cem\u003eJ Clin Microbiol\u003c/em\u003e \u003cstrong\u003e39\u003c/strong\u003e, 2308\u0026ndash;10 (2001).\u003c/li\u003e\n\u003cli\u003eHill, J. A. \u003cem\u003eet al.\u003c/em\u003e RNA sequencing of the in vivo human herpesvirus 6b transcriptome to identify targets for clinical assays distinguishing between latent and active infections. \u003cem\u003eJ Virol\u003c/em\u003e \u003cstrong\u003e93\u003c/strong\u003e, (2019).\u003c/li\u003e\n\u003cli\u003ePeddu, V. \u003cem\u003eet al.\u003c/em\u003e Inherited Chromosomally Integrated Human Herpesvirus 6 Demonstrates Tissue-Specific RNA Expression In Vivo That Correlates with an Increased Antibody Immune Response . \u003cem\u003eJ Virol\u003c/em\u003e \u003cstrong\u003e94\u003c/strong\u003e, (2019).\u003c/li\u003e\n\u003cli\u003eFredericks, D. N. \u0026amp; Relman, D. A. Sequence-based identification of microbial pathogens: a reconsideration of Koch\u0026rsquo;s postulates. \u003cem\u003eClin Microbiol Rev\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 18\u0026ndash;33 (1996).\u003c/li\u003e\n\u003cli\u003eFisher, C. E. \u003cem\u003eet al.\u003c/em\u003e The Serum Galactomannan Index Predicts Mortality in Hematopoietic Stem Cell Transplant Recipients With Invasive Aspergillosis. \u003cem\u003eClinical Infectious Diseases\u003c/em\u003e \u003cstrong\u003e57\u003c/strong\u003e, 1001\u0026ndash;1004 (2013).\u003c/li\u003e\n\u003cli\u003eWaghmare, A. \u003cem\u003eet al.\u003c/em\u003e Supplemental Oxygen\u0026ndash;Free Days in Hematopoietic Cell Transplant Recipients With Respiratory Syncytial Virus. \u003cem\u003eJ Infect Dis\u003c/em\u003e \u003cstrong\u003e216\u003c/strong\u003e, 1227\u0026ndash;1234 (2017).\u003c/li\u003e\n\u003cli\u003eHarris, B., Lowy, F. D., Stover, D. E. \u0026amp; Arcasoy, S. M. Diagnostic Bronchoscopy in Solid-Organ and Hematopoietic Stem Cell Transplantation. \u003cem\u003eAnn Am Thorac Soc\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 39\u0026ndash;49 (2013).\u003c/li\u003e\n\u003cli\u003eZerr, D. M. \u003cem\u003eet al.\u003c/em\u003e Human Herpesvirus 6 Reactivation and Encephalitis in Allogeneic Bone Marrow Transplant Recipients. \u003cem\u003eClinical Infectious Diseases\u003c/em\u003e \u003cstrong\u003e33\u003c/strong\u003e, 763\u0026ndash;771 (2001).\u003c/li\u003e\n\u003cli\u003eDobin, A. \u003cem\u003eet al.\u003c/em\u003e STAR: ultrafast universal RNA-seq aligner. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cstrong\u003e29\u003c/strong\u003e, 15\u0026ndash;21 (2013).\u003c/li\u003e\n\u003cli\u003eLove, M. I., Huber, W. \u0026amp; Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. \u003cem\u003eGenome Biol\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 550 (2014).\u003c/li\u003e\n\u003cli\u003eReiner, A., Yekutieli, D. \u0026amp; Benjamini, Y. Identifying differentially expressed genes using false discovery rate controlling procedures. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e, 368\u0026ndash;75 (2003).\u003c/li\u003e\n\u003cli\u003eRobinson, M. D., McCarthy, D. J. \u0026amp; Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cstrong\u003e26\u003c/strong\u003e, 139\u0026ndash;40 (2010).\u003c/li\u003e\n\u003cli\u003eSubramanian, A. \u003cem\u003eet al.\u003c/em\u003e Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e \u003cstrong\u003e102\u003c/strong\u003e, 15545\u0026ndash;15550 (2005).\u003c/li\u003e\n\u003cli\u003eHart, S. N., Therneau, T. M., Zhang, Y., Poland, G. A. \u0026amp; Kocher, J.-P. Calculating sample size estimates for RNA sequencing data. \u003cem\u003eJ Comput Biol\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 970\u0026ndash;8 (2013).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"HHV-6, transplant, pulmonary, pneumonia, RNA-sequencing","lastPublishedDoi":"10.21203/rs.3.rs-3361983/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3361983/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLimited understanding of the immunopathogenesis of human herpesvirus 6B (HHV-6B) has prevented its acceptance as a pulmonary pathogen after hematopoietic cell transplantation (HCT). We conducted a prospective multicenter study of patients undergoing bronchoalveolar lavage (BAL) for pneumonia after allogeneic HCT. We tested blood and BAL fluid (BALF) for HHV-6B DNA and mRNA transcripts associated with lytic infection and performed RNA-seq on paired blood. Among 116 participants, HHV-6B DNA was detected in 37% of BALs, 49% of which had HHV-6B mRNA detection. We established an HHV-6B DNA threshold (≥2.3 log\u003csub\u003e10\u003c/sub\u003e\u0026nbsp;copies/ml in BALF) that was highly predictive of HHV-6B mRNA detection and increased risk for death from respiratory failure (adjusted HR, 2.35; 95% CI, 1.08-5.11). Participants with HHV-6B DNA in BALF exhibited distinct host gene expression signatures, notable for enriched interferon signaling pathways in participants clinically diagnosed with idiopathic pneumonia. These data implicate HHV-6B as a pulmonary pathogen after allogeneic HCT.\u003c/p\u003e","manuscriptTitle":"Human herpesvirus 6B (HHV-6B) detection and genome-wide host expression profiles implicate HHV-6B as a pulmonary pathogen after hematopoietic cell transplantation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-09-28 15:38:18","doi":"10.21203/rs.3.rs-3361983/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a4a48a58-f793-4d62-99dc-84b27f1680d7","owner":[],"postedDate":"September 28th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":24919227,"name":"Health sciences/Medical research/Translational research"},{"id":24919228,"name":"Biological sciences/Microbiology/Virology/Herpes virus"},{"id":24919229,"name":"Biological sciences/Cancer/Haematological cancer"},{"id":24919230,"name":"Biological sciences/Microbiology/Infectious-disease diagnostics"},{"id":24919231,"name":"Health sciences/Diseases/Infectious diseases/Viral infection"}],"tags":[],"updatedAt":"2024-01-22T18:58:16+00:00","versionOfRecord":{"articleIdentity":"rs-3361983","link":"https://doi.org/10.1038/s41467-024-44828-9","journal":{"identity":"nature-communications","isVorOnly":false,"title":"Nature Communications"},"publishedOn":"2024-01-16 05:00:00","publishedOnDateReadable":"January 16th, 2024"},"versionCreatedAt":"2023-09-28 15:38:18","video":"","vorDoi":"10.1038/s41467-024-44828-9","vorDoiUrl":"https://doi.org/10.1038/s41467-024-44828-9","workflowStages":[]},"version":"v1","identity":"rs-3361983","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3361983","identity":"rs-3361983","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}